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Exam Code: P2090-068 Practice test 2022 by team
IBM Information Management Informix Technical Mastery Test v3
IBM Information approach
Killexams : IBM Information approach - BingNews Search results Killexams : IBM Information approach - BingNews Killexams : IBM’s human-centered approach is the only big tech blueprint AI startups should follow

IBM’s gone by just its initials for so long that many of us have to stop and think about what the letters stand for. International Business Machines.

I was reminded of the corporation’s singular focus last week during the TNW 2022 Conference when Seth Dobrin, IBM’s first chief AI officer, took the stage to talk about artificial intelligence.

As Dobrin put it, IBM “doesn’t do consumer AI.” You won’t be downloading IBM’s virtual assistant for your smart phone anytime soon. Big Blue won’t be getting into the selfie app AI filter game.

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Simply put, IBM’s here to provide value for its clients and partners and to create AI models that make human lives easier, better, or both.

That’s all pretty easy to say. But how does a company that’s not focused on creating products and services for the individual consumer actually walk that kind of talk?

According Dobrin, it’s not hard: care about how individual humans will be affected by the models you monetize:

We’re very stringent about the type of data we will ingest and make money from.

During a discussion with the Financial Times’ Tim Bradshaw during the conference, Dobrin used the example of large-parameter models such as GPT-3 and DALL-E 2 as a way to describe IBM’s approach.

He described those models as “toys,” and for good reason: they’re fun to play with, but they’re ultimately not very useful. They’re prone to unpredictability in the form of nonsense, hate-speech, and the potential to output private personal information. This makes them dangerous to deploy outside of laboratories.

However, Dobrin told Bradshaw and the audience that IBM was also working on a similar system. He referred to these agents as “foundational models,” meaning they can be used for multiple applications once developed and trained.

The IBM difference, however, is that the company is taking a human-centered approach to the development of its foundational models.

Under Dobrin’s leadership, the company’s cherry-picking datasets from a variety of sources and then applying internal terms and conditions to them prior to their integration into models or systems.

It’s one thing if GPT-3 accidentally spits out something offensive, these kinds of things are expected in laboratories. But it’s an entirely different situation when, as a hypothetical example, a bank’s production language model starts outputting nonsense or private information to customers.

Luckily, IBM (a company that works with corporations across a spectrum of industries including banking, transportation, and energy) doesn’t believe in cramming a giant database of unchecked data into a model and hoping for the best.

Which brings us to what’s perhaps the most interesting take away from Dobrin’s chat with Bradshaw: “be ready for regulations.”

As the old saying goes: BS in, BS out. If you’re not in control of the data you’re training with, life’s going to get hard for your AI startup come regulation time.

And the Wild West of AI acquisitions is going to come to an end soon as more and more regulatory bodies seek to protect citizens from predatory AI companies and corporate overreach.

If your AI startup creates models that won’t or can’t be compliant in time for use in the EU or US once the regulation hammers fall, your chances of selling them to or getting acquired by a corporation that does business internationally are slim to none.

No matter how you slice it, IBM’s an outlier. It and Dobrin apparently relish the idea of delivering compliance-ready solutions that help protect people’s privacy.

While the rest of big tech spends billions of dollars building eco-harming models that serve no purpose other than to pass arbitrary benchmarks, IBM’s more worried about outcomes than speculation.

And that’s just weird. That’s not how the majority of the industry does business.

IBM and Dobrin are trying to redefine what big tech’s position in the AI sector is. And, it turns out, when your bottom line isn’t driven by advertising revenue, subscriber numbers, or future hype, you can build solutions that are as efficacious as they are ethical.

And that leaves the vast majority of people in the AI startup world with some questions to answer.

Is your startup ready for the future? Are you training models ethically, considering human outcomes, and able to explain the biases baked into your systems? Can your models be made GDPR, EU AI, and Illinois BIPA compliant?

If the current free-for-all dies out and VCs stop throwing money at prediction models and other vaporware or prestidigitation-based products, can your models still provide business value?

There’s probably still a little bit of money to be made for companies and startups who leap aboard the hype train, but there’s arguably a whole lot more to be made for those whose products can actually withstand an AI winter.

Human-centered AI technologies aren’t just a good idea because they make life better for humans, they’re also the only machine learning applications worth betting on over the long haul.

When the dust settles, and we’re all less impressed by the prestidigitation and parlor tricks that big tech’s spending billions of dollars on, IBM will still be out here using our planet’s limited energy resources to develop solutions with individual human outcomes in mind.

That’s the very definition of “sustainability,” and why IBM’s poised to become the defacto technological leader in the global artificial intelligence community under Dobrin’s so-far expert leadership.

Mon, 20 Jun 2022 09:57:00 -0500 en text/html
Killexams : Top 10 data lake solution vendors in 2022

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today!

As the world becomes increasingly data-driven, businesses must find suitable solutions to help them achieve their desired outcomes. Data lake storage has garnered the attention of many organizations that need to store large amounts of unstructured, raw information until it can be used in analytics applications.

The data lake solution market is expected to grow rapidly in the coming years and is driven by vendors that offer cost-effective, scalable solutions for their customers.

Learn more about data lake solutions, what key features they should have and some of the top vendors to consider this year. 

What is a data lake solution?

A data lake is defined as a single, centralized repository that can store massive amounts of unstructured and semi-structured information in its native, raw form. 


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It’s common for an organization to store unstructured data in a data lake if it hasn’t decided how that information will be used. Some examples of unstructured data include images, documents, videos and audio. These data types are useful in today’s advanced machine learning (ML) and advanced analytics applications.

Data lakes differ from data warehouses, which store structured, filtered information for specific purposes in files or folders. Data lakes were created in response to some of the limitations of data warehouses. For example, data warehouses are expensive and proprietary, cannot handle certain business use cases an organization must address, and may lead to unwanted information homogeneity.

On-premise data lake solutions were commonly used before the widespread adoption of the cloud. Now, it’s understood that some of the best hosts for data lakes are cloud-based platforms on the edge because of their inherent scalability and considerably modular services. 

A 2019 report from the Government Accountability Office (GAO) highlights several business benefits of using the cloud, including better customer service and the acquisition of cost-effective options for IT management services.

Cloud data lakes and on-premise data lakes have pros and cons. Businesses should consider cost, scale and available technical resources to decide which type is best.

Read more about data lakes: What is a data lake? Definition, benefits, architecture and best practices

5 must-have features of a data lake solution

It’s critical to understand what features a data lake offers. Most solutions come with the same core components, but each vendor may have specific offerings or unique selling points (USPs) that could influence a business’s decision.

Below are five key features every data lake should have:

1. Various interfaces, APIs and endpoints

Data lakes that offer diverse interfaces, APIs and endpoints can make it much easier to upload, access and move information. These capabilities are important for a data lake because it allows unstructured data for a wide range of use cases, depending on a business’s desired outcome.

2. Support for or connection to processing and analytics layers

ML engineers, data scientists, decision-makers and analysts benefit most from a centralized data lake solution that stores information for easy access and availability. This characteristic can help data professionals and IT managers work with data more seamlessly and efficiently, thus improving productivity and helping companies reach their goals.

3. Robust search and cataloging features

Imagine a data lake with large amounts of information but no sense of organization. A viable data lake solution must incorporate generic organizational methods and search capabilities, which provide the most value for its users. Other features might include key-value storage, tagging, metadata, or tools to classify and collect subsets of information.

4. Security and access control

Security and access control are two must-have features with any digital tool. The current cybersecurity landscape is expanding, making it easier for threat actors to exploit a company’s data and cause irreparable damage. Only certain users should have access to a data lake, and the solution must have strong security to protect sensitive information.

5. Flexibility and scalability

More organizations are growing larger and operating at a much faster rate. Data lake solutions must be flexible and scalable to meet the ever-changing needs of modern businesses working with information.

Also read: Unlocking analytics with data lake and graph analysis

Top 10 data lake solution vendors in 2022

Some data lake solutions are best suited for businesses in certain industries. In contrast, others may work well for a company of a particular size or with a specific number of employees or customers. This can make choosing a potential data lake solution vendor challenging. 

Companies considering investing in a data lake solution this year should check out some of the vendors below.

1. Amazon Web Services (AWS)

The AWS Cloud provides many essential tools and services that allow companies to build a data lake that meets their needs. The AWS data lake solution is widely used, cost-effective and user-friendly. It leverages the security, durability, flexibility and scalability that Amazon S3 object storage offers to its users. 

The data lake also features Amazon DynamoDB to handle and manage metadata. AWS data lake offers an intuitive, web-based console user interface (UI) to manage the data lake easily. It also forms data lake policies, removes or adds data packages, creates manifests of datasets for analytics purposes, and features search data packages.

2. Cloudera

Cloudera is another top data lake vendor that will create and maintain safe, secure storage for all data types. Some of Cloudera SDX’s Data Lake Service capabilities include:

  • Data schema/metadata information
  • Metadata management and governance
  • Compliance-ready access auditing
  • Data access authorization and authentication for improved security

Other benefits of Cloudera’s data lake include product support, downloads, community and documentation. GSK and Toyota leveraged Cloudera’s data lake to garner critical business intelligence (BI) insights and manage data analytics processes.

3. Databricks 

Databricks is another viable vendor, and it also offers a handful of data lake alternatives. The Databricks Lakehouse Platform combines the best elements of data lakes and warehouses to provide reliability, governance, security and performance.

Databricks’ platform helps break down silos that normally separate and complicate data, which frustrates data scientists, ML engineers and other IT professionals. Aside from the platform, Databricks also offers its Delta Lake solution, an open-format storage layer that can Excellerate data lake management processes. 

4. Domo

Domo is a cloud-based software company that can provide big data solutions to all companies. Users have the freedom to choose a cloud architecture that works for their business. Domo is an open platform that can augment existing data lakes, whether it’s in the cloud or on-premise. Users can use combined cloud options, including:

  • Choosing Domo’s cloud
  • Connecting to any cloud data
  • Selecting a cloud data platform

Domo offers advanced security features, such as BYOK (bring your own key) encryption, control data access and governance capabilities. Well-known corporations such as Nestle, DHL, Cisco and Comcast leverage the Domo Cloud to better manage their needs.

5. Google Cloud

Google is another big tech player offering customers data lake solutions. Companies can use Google Cloud’s data lake to analyze any data securely and cost-effectively. It can handle large volumes of information and IT professionals’ various processing tasks. Companies that don’t want to rebuild their on-premise data lakes in the cloud can easily lift and shift their information to Google Cloud. 

Some key features of Google’s data lakes include Apache Spark and Hadoop migration, which are fully managed services, integrated data science and analytics, and cost management tools. Major companies like Twitter, Vodafone, Pandora and Metro have benefited from Google Cloud’s data lakes.

6. HP Enterprise

Hewlett Packard Enterprise (HPE) is another data lake solution vendor that can help businesses harness the power of their big data. HPE’s solution is called GreenLake — it offers organizations a truly scalable, cloud-based solution that simplifies their Hadoop experiences. 

HPE GreenLake is an end-to-end solution that includes software, hardware and HPE Pointnext Services. These services can help businesses overcome IT challenges and spend more time on meaningful tasks. 

7. IBM

Business technology leader IBM also offers data lake solutions for companies. IBM is well-known for its cloud computing and data analytics solutions. It’s a great choice if an operation is looking for a suitable data lake solution. IBM’s cloud-based approach operates on three key principles: embedded governance, automated integration and virtualization.

These are some data lake solutions from IBM: 

  • IBM Db2
  • IBM Db2 BigSQL
  • IBM Netezza
  • IBM Watson Query
  • IBM Watson Knowledge Catalog
  • IBM Cloud Pak for Data

With so many data lakes available, there’s surely one to fit a company’s unique needs. Financial services, healthcare and communications businesses often use IBM data lakes for various purposes.

8. Microsoft Azure

Microsoft offers its Azure Data Lake solution, which features easy storage methods, processing, and analytics using various languages and platforms. Azure Data Lake also works with a company’s existing IT investments and infrastructure to make IT management seamless.

The Azure Data Lake solution is affordable, comprehensive, secure and supported by Microsoft. Companies benefit from 24/7 support and expertise to help them overcome any big data challenges they may face. Microsoft is a leader in business analytics and tech solutions, making it a popular choice for many organizations.

9. Oracle

Companies can use Oracle’s Big Data Service to build data lakes to manage the influx of information needed to power their business decisions. The Big Data Service is automated and will provide users with an affordable and comprehensive Hadoop data lake platform based on Cloudera Enterprise. 

This solution can be used as a data lake or an ML platform. Another important feature of Oracle is it is one of the best open-source data lakes available. It also comes with Oracle-based tools to add even more value. Oracle’s Big Data Service is scalable, flexible, secure and will meet data storage requirements at a low cost.

10. Snowflake

Snowflake’s data lake solution is secure, reliable and accessible and helps businesses break down silos to Excellerate their strategies. The top features of Snowflake’s data lake include a central platform for all information, fast querying and secure collaboration.

Siemens and Devon Energy are two companies that provide testimonials regarding Snowflake’s data lake solutions and offer positive feedback. Another benefit of Snowflake is its extensive partner ecosystem, including AWS, Microsoft Azure, Accenture, Deloitte and Google Cloud.

The importance of choosing the right data lake solution vendor 

Companies that spend extra time researching which vendors will offer the best enterprise data lake solutions for them can manage their information better. Rather than choose any vendor, it’s best to consider all options available and determine which solutions will meet the specific needs of an organization.

Every business uses information, some more than others. However, the world is becoming highly data-driven — therefore, leveraging the right data solutions will only grow more important in the coming years. This list will help companies decide which data lake solution vendor is right for their operations.

Read next: Get the most value from your data with data lakehouse architecture

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Fri, 15 Jul 2022 09:40:00 -0500 Shannon Flynn en-US text/html
Killexams : IBM Acquires Israeli Data Observability Startup

American tech giant IBM announced on Wednesday that it had acquired its acquisition of Tel-Aviv-based company, a data observability software company that helps organizations with data issues. works to help companies alleviate data errors, pipeline failures, and poor data quality before the company’s bottom line is impacted. By acquiring, IBM hopes to strengthen its software portfolio across artificial intelligence, data, and automation, ultimately ensuring data stays secure at all times.

Founded in 2018 by CEO Josh Benamram, Victor Shafran, and CTO Evgeny Shulman, has developed a unified data pipeline observability solution that’s built for data engineers. has an open and extendable approach that allows data engineering teams to easily integrate and gain observability into their data infrastructure. In partnering with IBM, will be able to expand its data integration capabilities to meet the needs of more commercial data solutions. IBM will also benefit from the acquisition, as’s software will partner with IBM Observability by Instana APM and IBM Watson Studio in addressing the full spectrum of observability across information technologies. marks IBM’s fifth acquisition in 2022. 

IBM’s acquisition comes at a time when during which the volume of data is growing at an unprecedented rate. Now more than ever, organizations are grappling with the challenges of managing healthy and high-quality data sets. Data observability is newly emerging as a prime solution for helping companies and engineers understand the status of their data and efficiently address and troubleshoot issues as they arise.

“Our clients are data-driven enterprises who rely on high-quality, trustworthy data to power their mission-critical processes. When they don’t have access to the data they need in any given moment, their business can grind to a halt,” said Daniel Hernandez, general manager for Data and AI, IBM. 

“With the addition of, IBM offers the most comprehensive set of observability capabilities for IT across applications, data, and machine learning, and is continuing to provide our clients and partners with the technology they need to deliver trustworthy data and AI at scale,” he added.  

Thu, 07 Jul 2022 08:10:00 -0500 en-US text/html
Killexams : IBM Aims to Capture Growing Market Opportunity for Data Observability with Acquisition

Acquisition helps enterprises catch "bad data" at the source

Extends IBM's leadership in observability to the full stack of capabilities for IT -- across infrastructure, applications, data and machine learning

ARMONK, N.Y., July 6, 2022  /CNW/ -- IBM (NYSE: IBM) today announced it has acquired, a leading provider of data observability software that helps organizations fix issues with their data, including errors, pipeline failures and poor quality — before it impacts their bottom-line. Today's news further strengthens IBM's software portfolio across data, AI and automation to address the full spectrum of observability and helps businesses ensure that trustworthy data is being put into the right hands of the right users at the right time. is IBM's fifth acquisition in 2022 as the company continues to bolster its hybrid cloud and AI skills and capabilities. IBM has acquired more than 25 companies since Arvind Krishna became CEO in April 2020.

As the volume of data continues to grow at an unprecedented pace, organizations are struggling to manage the health and quality of their data sets, which is necessary to make better business decisions and gain a competitive advantage. A rapidly growing market opportunity, data observability is quickly emerging as a key solution for helping data teams and engineers better understand the health of data in their system and automatically identify, troubleshoot and resolve issues, like anomalies, breaking data changes or pipeline failures, in near real-time. According to Gartner, every year poor data quality costs organizations an average $12.9 million. To help mitigate this challenge, the data observability market is poised for strong growth.1

Data observability takes traditional data operations to the next level by using historical trends to compute statistics about data workloads and data pipelines directly at the source, determining if they are working, and pinpointing where any problems may exist. When combined with a full stack observability strategy, it can help IT teams quickly surface and resolve issues from infrastructure and applications to data and machine learning systems.'s open and extendable approach allows data engineering teams to easily integrate and gain observability into their data infrastructure. This acquisition will unlock more resources for to expand its observability capabilities for broader integrations across more of the open source and commercial solutions that power the modern data stack. Enterprises will also have full flexibility in how to run, whether as-a-Service (SaaS) or a self-hosted software subscription.

The acquisition of builds on IBM's research and development investments as well as strategic acquisitions in AI and automation. By using with IBM Observability by Instana APM and IBM Watson Studio, IBM is well-positioned to address the full spectrum of observability across IT operations.

For example, capabilities can alert data teams and engineers when the data they are using to fuel an analytics system is incomplete or missing. In common cases where data originates from an enterprise application, Instana can then help users quickly explain exactly where the missing data originated from and why an application service is failing. Together, and IBM Instana provide a more complete and explainable view of the entire application infrastructure and data platform system, which can help organizations prevent lost revenue and reputation.

"Our clients are data-driven enterprises who rely on high-quality, trustworthy data to power their mission-critical processes. When they don't have access to the data they need in any given moment, their business can grind to a halt," said Daniel Hernandez, General Manager for Data and AI, IBM. "With the addition of, IBM offers the most comprehensive set of observability capabilities for IT across applications, data and machine learning, and is continuing to provide our clients and partners with the technology they need to deliver trustworthy data and AI at scale."

Data observability solutions are also a key part of an organization's broader data strategy and architecture. The acquisition of further extends IBM's existing data fabric solution  by helping ensure that the most accurate and trustworthy data is being put into the right hands at the right time – no matter where it resides.

"You can't protect what you can't see, and when the data platform is ineffective, everyone is impacted –including customers," said Josh Benamram, Co-Founder and CEO, "That's why global brands such as FanDuel, Agoda and Trax Retail already rely on to remove bad data surprises by detecting and resolving them before they create costly business impacts. Joining IBM will help us scale our software and significantly accelerate our ability to meet the evolving needs of enterprise clients."

Headquartered in Tel Aviv, Israel, employees will join IBM Data and AI, further building on IBM's growing portfolio of Data and AI products, including its IBM Watson capabilities and IBM Cloud Pak for Data. Financial details of the deal were not disclosed. The acquisition closed on June 27, 2022.

To learn more about and how this acquisition enhances IBM's data fabric solution and builds on its full stack of observability software, you can read our blog about the news or visit here:

About is a product-driven technology company that provides a proactive data observability platform, which empowers data engineering teams to deliver reliable and trustworthy data. removes bad data surprises such as data incompleteness, anomalies, and breaking data changes by detecting and resolving issues before they create costly business impacts.'s proactive approach ties into all stages of your data pipelines, beginning with your source data, through ingestion, transformation, and data access. serves organizations throughout the globe, including some of the world's largest companies in entertainment, technology, and communications. Our focus is on enabling customers to extract the maximum value from their strategic data investments. is backed by leading VCs Accel, Blumberg Capital, Lerer Hippeau, Differential Ventures, Ubiquity Ventures, Bessemer Venture Partners, Hyperwise, and F2. To learn more, visit

About IBM

IBM is a leading global hybrid cloud and AI, and business services provider, helping clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,800 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently, and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM's legendary commitment to trust, transparency, responsibility, inclusivity, and service. For more information, visit

Media Contact:
Sarah Murphy
IBM Communications
[email protected]

1 [1] Source: Smarter with Gartner, "How to Excellerate Your Data Quality," Manasi Sakpal, [July 14, 2021]

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.


Wed, 06 Jul 2022 00:00:00 -0500 en text/html
Killexams : IBM of 1956 is much different from IBM today

Like people, companies change over time.

The IBM that came to Rochester in 1956 is very different from the IBM that today is considered the city's second largest employer.

"It was a very special place," says Chub Stewart, who worked at Big Blue for 30 years before retiring in 1986. "It was the greatest company in the world to work for."

The IBM-Rochester of today remains one of the world's leading technology companies and a component of the Dow Jones Industrial Average. But many rounds of layoffs in the past decade have reduced the staff to perhaps half of its peak employment of 8,000, although the company won’t provide the current employment count. The last confirmed employee count was 4,200 in 2008.

Company-wide changes, while making IBM more flexible, also have made it a more uncertain place for employees to work.

They’re indicative of the trends in the American economy in the past 40 years: a growing emphasis on shareholder return, jobs moving overseas and a fast-changing environment for remaining workers. A look at the company’s early years shows how much has changed.

Those early years started in the former Red Owl grocery store in downtown Rochester while the facility was built on the northwest side of the city.

When IBM announced it was opening a site in Rochester in the 1950s, it quickly became the place to get a job.

"Every valedictorian from every small town around here applied at IBM. They got the pick of the litter, … the very best of what southeast Minnesota had to offer," says Stewart.

Retirees recall company picnics larger than most county fairs, including midway rides and entertainment. The star of the "Lone Ranger" TV show appeared at the 1962 picnic. Employees received engraved silver spoons for the arrival of a new child. There were massive Christmas parties where every child got a present.

With its shining blue exterior, the complex was filled with loyal employees wearing suits with white shirts and ties until the 1980s.

Think "Mad Men" without the alcohol.

IBM-Rochester began primarily as a manufacturing site focused on complex machinery with a lot of moving parts, such as collators, punch card readers and check-handling "proof" machines for banks.

"It was one big family," says Dave Marshik, who retired in 2000 after 34 years at IBM-Rochester. "I think I knew the first name of everyone who worked there."

It was Rochester's top employer in its early years. In 1966, Mayo Clinic caught up to it in employee count, when each employed 3,600 workers. It was a time of rapid growth for both operations. Mayo pulled ahead in 1967 with 3,850 compared to IBM's 3,800.

Big Blue's Rochester campus has grown and shrunk through the years depending on the market. At its peak in the early 1990s, it had more than 8,000 employees.

For much of its history in Rochester, IBM was known as a place where most of its employees would spend all of their careers.

President Tom Watson Sr. and later his son, Tom Watson, Jr., fired or laid off employees only in extreme circumstances. They called it the Full Employment Practice. Both Watsons often spoke of the value of the individual at IBM and a loyalty to their employees.

"No subject occupies more executive time at IBM than the well-being of our employees and their families," Watson Jr. is quoted as saying in 1958.

In Rochester, a large number of employees stayed at IBM for their entire careers until retiring comfortably.

"As long as you kept your nose clean, you were there for life," remembers Art T. Maley, who retired in 1991 after 36 years with Big Blue.

Jeff Trachey, who started at IBM in 1974, says the company always worked hard to find the right fit for workers, including offering extensive education and re-training.

"If you didn't perform well, maybe you were not in the right job," he says. "Attrition was really low."

Of course, that kind of approach sometimes meant a lot of physical shifting around of employees, leading to the saying in the 1980s that IBM stood for "I've Been Moved."

As the market for mainframe computers grew, IBM earned the nickname of "Snow White." That's because it consistently led smaller competing companies dubbed the "seven dwarves."

Rochester’s employees played a key role in developing the company’s vaunted technology to put IBM in the spot.

A development lab opened early on here, and Rochester began a long tradition of creating innovative products, including optical readers, System/3 business computer, point-of-sale retail systems, the 50-pound 5100 Portable Computer, System/36 minicomputer and disk drives.

Within IBM, Rochester developed a reputation for being a distant but extremely creative outpost. It also came across as little rebellious. That earned it the internal nickname of "Fortress Rochester" in the 1980s.

It was in that environment that the popular AS/400 system was developed and then released in 1988. The local development team of about 30 people reportedly operated without official funding and was free of interference from Big Blue's corporate office.

"That was a fight all the way for Rochester. We spent more time fighting IBM than working with customers," remembers Marshik.

That successful mid-range system and its many descendants became synonymous with Rochester.

"At times in Rochester's history, it was advantageous to let people think we were working on projects that were blessed by the corporation," wrote Dr. Frank Soltis in his "Fortress Rochester" book. "In reality, the IBM corporation rarely knew or cared about what we were doing, and we in our fortress preferred to keep it that way."

Local retirees say that in those days there was "a wink and nod system" that meant there was almost always money on the side to fund research for "grand and glorious projects" that might or might not work.

One such Rochester story is that of an early project using magnetic tape strips to store information, such as is done on credit cards today. A Rochester researcher took some tape home, had his wife iron it onto some cardboard and began working out how to store information on it.

But technological developments and increasingly aggressive competition over decades started to change the fairy tale for Big Blue and the Rochester family.

As the playing field changed with the rise of PCs and nimble competition like Microsoft and Apple in the 1980s and 1990s, the slow-moving and tradition-oriented IBM struggled and started to lose ground.

It took dramatic cuts and streamlined processes to "make the elephant dance." It was during this time in the early 1990s that the Watsons' lifetime employment standard was reversed.

The first large-scale example of the change was a company-wide round of layoffs/early retirements of 60,000 employees in 1993 under the watch of CEO Lou Gerstner Jr. Most industry watchers still consider it the largest tech layoff in modern history.

In Rochester, about 1,600 to 2,000 people lost their jobs at IBM during that time.

Gerstner is widely hailed as the man who kept the company from becoming obsolete and brought it back into the game. However, he is also known as the man who wiped out much of what the Watsons created in order to save the company.

"He basically destroyed the IBM culture. Two years into his reign, the IBM culture as we knew it was gone, … finished. It never really recovered," says Bob Djurdjevic of Hawaii-based Annex Research, a longtime technology analyst and expert on IBM. "IBM just became another girl on the street, where before it used to be very special."

The changes swept through Rochester like a storm and left behind a very different IBM, one that was more successful in the market but no longer a beloved employer.

"The big family disappeared overnight. There was so much depression. Everybody was afraid they were going to be next," says Marshik.

While it was harsh, some former IBMers say they can see why drastic changes were needed.

"The transition has been painful at times as the skills of many employees have no longer been needed in the same quantity," says Bill Plummer, who retired in 1990. "IBM had a custom of not laying off employees, which led them down some costly paths as it tried to develop products that fit the skills of the workers but were becoming obsolete."

Layoffs are now a common occurrence at the modern Big Blue. For instance, in each of the past four years, IBM has had substantial layoff actions in the February/March period, including some in Rochester. However, the company no longer provides exact layoff numbers or the number of remaining employees in Rochester.

Many of those jobs are going overseas as IBM, like other American companies, seeks lower-cost employees and a share of foreign markets.

"The technology giant has been steadily building its work force in India and other locations while reducing the number of workers based in the U.S.," read a Wall Street Journal story about a March 2009 layoff action. "Foreign workers accounted for 71 percent of Big Blue's nearly 400,000 employees at the start of the year, up from about 65 percent in 2006."

Some employees, speaking anonymously, say life at IBM today is uncertain, with people worried about their jobs. They say it became even more dispiriting when IBM stopped publicly discussing layoffs and then employment numbers at individual plants.

Instead of the tight-knit family of the past, many describe IBM's campus today as a train full of anxious passengers. People are asked to get off at each stop, but no one knows which stop theirs will be.

Bob Cringely, a well-known technology columnist and PBS contributor, has been vocal in his criticism of this IBM.

"It is a heartsick company today," he says. "There are a few people working there who are happy. The ones that are happy work in sales."

Of course, people who are unhappy do have a choice.

"It is not the IBM I knew," says Trachey. "However, everybody always has the option to vote with their feet and leave."

While morale is low among some employees, IBM has maintained a spot as a technology leader. And, in some heartening news for the Rochester plant, IBM recently introduced its new PureSystems line, an easy-to-deploy integrated system largely developed and manufactured in Rochester.

Other major developments continue to roll out from the 100-year-old company. Its stock prices are at historic highs, and dividends are plentiful.

That has made it popular with investors, including the well-known Warren Buffett. "It's a company that helps IT departments do their job better," he said in 2011 in unveiling a major investment there.

Still, it’s a different company for those who work there.

"The IBM of today is not the company I worked for," says Stewart simply.

Mon, 04 Jul 2022 01:00:00 -0500 en text/html
Killexams : Opinion: Why overturning Roe v. Wade is a huge win for big tech

No matter one’s moral or ethical stance on abortion — and we all know good people on both sides of the contentious issue — the rationale of the court’s majority decision is godsend to a lord of a different kind, the overlord of Big Tech.

Big Tech — or the GMAFIA, as author Amy Webb refers to them in her book “The Big Nine: How the Tech Titans and their Thinking Machines Can Warp Humanity,” which consists of Google, Microsoft, Amazon, Facebook, IBM and Apple, in America, and the BAT acronym for Baidu, Alibaba and Tencent in China — just scored a major victory in the Dobbs decision, and if you’re wondering what the politics and legality of abortion have to do with big data, read on.

The “rationale” of the majority’s opinion is fairly straightforward yet circuitously backward at the same time: since the word “abortion” does not appear in the Constitution, and the practice is allegedly not engrained in our history since the inception of the document at the turn of the 18th century, the court, whose duty it believes is to strictly adhere to the text of the Constitution, must remain neutral and therefore silent on the issue, and pass the decision back to the states. (Of course, nowhere in the Constitution is there any mention of sitting in the back or front of a bus, or a right to privacy in your iPhone, because of course, the Constitution is similarly “silent” on the words “buses” and iPhones. But let’s leave aside the esoteric judicial philosophical arguments for a moment and focus on more real-world implications in another realm of privacy: the digital realm.

What the court did in the Dobbs decision was take a huge sledgehammer to the “right to privacy,” because the right to an abortion, previously located in both the Fourth and Fourteenth amendments, emanates from the prior right of contraception, decided in the famous Griswold v. Connecticut case. (Yes, not too long ago, Connecticut criminalized the use of any contraception.)

In other words, what the court did was supremely sleight-of-hand clever, ruling, essentially, that no right to “privacy” could exist because, well, the word “privacy” doesn’t appear in the document. Yes, they did, and no I’m not kidding. But if there is no federal right to privacy, then what happens to the privacy of our data, to the minimal extent we have any now, if at all? You guessed right: it doesn’t exist at the constitutional level at all, and probably now never will. Beyond creepy stuff.

Of course, the conservative court majority will holler back, “If the American people want their data to be kept private, then it is up to Congress, and the various state legislatures to ensure such privacy, not unelected judges on the Supreme Court to just make it up!” But do you really think that Congress, already in the pocket of the gun lobby, the health insurance and pharmaceutical lobby, and the fossil fuel lobby, will have our interests in mind when drafting legislation, or the interests of the GMAFIA and the BAT? Something tells me Mark Zuckerberg and Jeff Bezos probably gave more to Congress last year than all of the readers of this paper combined did.

What constitutional scholars were hoping to see over the next few decades was a widening, not a constricting, of the circle of privacy articulated by the Supreme Court, in our bodily autonomy, consensual relationships, medical information and personal electronic data. But instead what the court did was sharply limit that circle of privacy protection, and they won’t stop there at abortion.

By extension then, your electronic data, your digital footprints, are now not only fair game, but open game, too. If you don’t have privacy in your bodily autonomy, then certainly you don’t have privacy in your electronic data, because, of course, the word “data” doesn’t appear in the Constitution, and the history of our country going back to the Founding Fathers is not known to afford a lot of data privacy, either.

Now of course, you might think, “what’s the big deal, since they know my data anyway?” I myself grew up in an age when clicking and checking “I agree” and “I hereby certify I have read the terms and conditions” was done by rote and in a split-second. But with the advent of machine learning and AI, the ramifications of this data harvesting are enormous. Did you know, for example, when you request an insurance quote, or credit card, the AI is not only paying attention to how many milliseconds you took to enter each data field (wait, did the user pause, and have to think about his income? Did his keystrokes indicate he typed one amount for income, then erased it, and type another, and was the second amount higher or lower? And how much battery life is on the user’s laptop? Is he the responsible type, close to full charge, or nearly running on empty? That data is telling to big tech, and telling on you, too, and among millions of other metrics being measured which humans can’t fathom, but machines can.)

When I teach courses on constitutional law and interpretation, I ask the students: should we limit interpreting the Constitution to paying attention only to the literal black letter text, or should we instead focus on the original purpose of the provision? Of course, a third option is to throw all that out, and also throw our hands up, and declare the Constitution a “living document” and thus to mean whatever the judges want it to mean in the moment. Of course, that is the wrong formulation of the question, because we shouldn’t limit ourselves to any one approach.

Judges are not grammarians, nor are they historians; we call them “judges” for a reason. Instead of a dogmatic approach to text, or to an original supposedly sole purpose, or a “whatever feels good by today’s standards however we get there with the legal logic” approach, the court should adopt all three methods. A justice should think, “Look, what the heck does the history suggest the framers were trying to accomplish here, what were the words they used to try to accomplish their goal, and how is the value of the goal they were trying to accomplish manifest today, in light of today’s society, economy and technology?” All are valid approaches.

To use just one approach is dumb, and myopic. But conservatives on the right and liberals on the left often play that “just one approach is the correct approach game,” but a third way is not to play it, and realize all three methods have merit and value in arriving at a cogent, coherent and holistic appreciation and understanding of the law, and how it should progress, not regress instead. It is legitimate for the Justices to be ushers … ushers into a brighter future, not throwbacks to a shadier past.

No matter what your stance on abortion, the right to privacy in America just suffered an enormous blow, and Big Tech is celebrating like you can’t believe. But guess who’s celebrating even more? Big Tech in China, and their godfather, the CCP.

Just wait and see, because they already do.

Ryan Knox, of New Haven, lived in Hong Kong from 2011 to 2014, and is an adjunct political science professor at the University of Bridgeport.

Sat, 16 Jul 2022 00:00:00 -0500 en-US text/html
Killexams : IBM Acquires to Boost Data Observability Capabilities

IBM is acquiring, a leading provider of data observability software that helps organizations fix issues with their data, including errors, pipeline failures, and poor quality. The acquisition further strengthens IBM's software portfolio across data, AI, and automation to address the full spectrum of observability. is IBM's fifth acquisition in 2022 as the company continues to bolster its hybrid cloud and AI skills and capabilities.'s open and extendable approach allows data engineering teams to easily integrate and gain observability into their data infrastructure.

This acquisition will unlock more resources for to expand its observability capabilities for broader integrations across more of the open source and commercial solutions that power the modern data stack.

Enterprises will also have full flexibility in how to run, whether as-a-Service (SaaS) or a self-hosted software subscription.

The acquisition of builds on IBM's research and development investments as well as strategic acquisitions in AI and automation. By using with IBM Observability by Instana APM and IBM Watson Studio, IBM is well-positioned to address the full spectrum of observability across IT operations.

"Our clients are data-driven enterprises who rely on high-quality, trustworthy data to power their mission-critical processes. When they don't have access to the data they need in any given moment, their business can grind to a halt," said Daniel Hernandez, general manager for data and AI, IBM. "With the addition of, IBM offers the most comprehensive set of observability capabilities for IT across applications, data and machine learning, and is continuing to provide our clients and partners with the technology they need to deliver trustworthy data and AI at scale."

The acquisition of further extends IBM's existing data fabric solution by helping ensure that the most accurate and trustworthy data is being put into the right hands at the right time—no matter where it resides.

Headquartered in Tel Aviv, Israel, employees will join IBM Data and AI, further building on IBM's growing portfolio of Data and AI products, including its IBM Watson capabilities and IBM Cloud Pak for Data. Financial details of the deal were not disclosed. The acquisition closed on June 27, 2022.

For more information about this news, visit

Mon, 11 Jul 2022 01:02:00 -0500 en text/html
Killexams : IBM to Acquire Randori to Boost Cybersecurity for IBM’s Hybrid Cloud


IBM plans to acquire Randori, a leading attack surface management (ASM) and offensive cybersecurity provider, further advancing IBM's Hybrid Cloud strategy and strengthening its portfolio of AI-powered cybersecurity products and services.

Randori helps clients continuously identify external facing assets, both on-premise or in the cloud, that are visible to attackers—and prioritize exposures which pose the greatest risk.   

"Our clients today are faced with managing a complex technology landscape of accelerating cyberattacks targeted at applications running across a variety of hybrid cloud environments—from public clouds, private clouds and on-premises," said Mary O'Brien, general manager, IBM Security. "In this environment, it is essential for organizations to arm themselves with attacker's perspective in order to help find their most critical blind spots and focus their efforts on areas that will minimize business disruption and damages to revenue and reputation."

Randori is IBM's fourth acquisition in 2022 as the company continues to bolster its hybrid cloud and AI skills and capabilities, including in cybersecurity. IBM has acquired more than 20 companies since Arvind Krishna became CEO in April 2020.

Randori is a hacker led company, with software to help security teams discover gaps, assess risks, and Excellerate their security posture over time by delivering an authentic attack experience at scale.

Designed to help security teams zero in on previously unknown exposure points, Randori's unique attack surface management solution takes into account the logic of an adversary based on real-world attacks—and is the only one to prioritize based on level of risk as well as the attractiveness of an asset to potential attackers using their proprietary scoring system.

Their unique approach has led to the development of a cloud native solution that provides better prioritization of vulnerabilities and reduces noise by focusing on customers' unique attack surface. 

Upon close of the acquisition, IBM plans to integrate Randori's attack surface management software with the extended detection and response (XDR) capabilities of IBM Security QRadar.

By feeding insights from Randori into QRadar XDR, security teams will be able to leverage real-time attack surface visibility for intelligent alert triage, threat hunting, and incident response. This can help eliminate the need for customers to manually monitor new critical applications and respond quickly when new issues or emerging threats arise on their perimeter.

Randori also provides businesses with a solution that uniquely combines attack surface management with continuous automated red teaming (CART) to stress test defenses and incident response teams. Upon close, IBM will leverage Randori to compliment X-Force Red's elite hacker lead offensive security services while further enriching QRadar XDR detection and response capabilities.

This will allow more global customers to benefit from a top-tier attack experience that helps uncover where organizations are most vulnerable. Randori insights will also be leveraged by IBM's Managed Security Services to help Excellerate threat detection for thousands of clients.

For more information about this news, visit

Mon, 27 Jun 2022 01:08:00 -0500 Stephanie Simone en text/html
Killexams : GigaSpaces Joins Forces with IBM and Wix to Offer an Open Platform that Dramatically Accelerates Enterprise Digital Innovation No result found, try new keyword!GigaSpaces is joining forces with IBM and Wix to design a joint Digital Acceleration Hub (DAH), that will accelerate the pace at ... Wed, 06 Jul 2022 02:42:00 -0500 en text/html Killexams : Data Storage Market Is Likely to Experience a Strong Growth During 2022-2026 with Top Countries Data

The MarketWatch News Department was not involved in the creation of this content.

The "Data Storage Market" Research Report gives an inside and out outline and experiences into the market's size, incomes, different sections and drivers of improvement, as well as restricting elements and provincial modern presence. The objective of the statistical surveying study is to totally assess the 'Data Storage Sector' and get a survey makes sense of the business and its business possibilities. The concentrate likewise inspects the effect of COVID-19 on the business and income examinations when the pestilence. As per this, the client gets broad information on the business and firm from an earlier time, present, and future points of view, permitting them to put away cash and convey assets admirably.

To Know How Covid-19 Pandemic and Russia Ukraine War Will Impact This Market

Data Storage in this report refers to an Enterprise Storage System as a set of storage elements, including controllers, cables, and (in some instances) host bus adapters, associated with three or more disks. A system may be located outside of or within a server cabinet and the average cost of the disk storage systems does not include infrastructure storage hardware (i.e. switches) and non-bundled storage software.

Market Analysis and Insights: Global and United States Data Storage Market

This report focuses on global and United States Data Storage market, also covers the segmentation data of other regions in regional level and county level.

Due to the COVID-19 pandemic, the global Data Storage market size is estimated to be worth USD million in 2022 and is forecast to a readjusted size of USD million by 2026 with a CAGR of % during the review period. Fully considering the economic change by this health crisis, by Type, All-Flash Arrays accounting for % of the Data Storage global market in 2021, is projected to value USD million by 2026, growing at a revised % CAGR in the post-COVID-19 period. While by Application, IT and Telecom was the leading segment, accounting for over percent market share in 2021, and altered to an % CAGR throughout this forecast period.

In the Spanish market, the main Data Storage players include HPE, NetApp, Dell EMC, IBM, Pure Storage, etc. The top five Data Storage players account for approximately 59% of the total market. In terms of type, Hybrid Storage Arrays is the largest segment, with a share over 42%. And in terms of application, the largest application is IT and Telecom, followed by BSFI.

Global Data Storage Scope and Market Size

Data Storage market is segmented by region (country), players, by Type and by Application. Players, stakeholders, and other participants in the global Data Storage market will be able to gain the upper hand as they use the report as a powerful resource. The segmental analysis focuses on revenue and forecast by region (country), by Type and by Application for the period 2017-2026.

For United States market, this report focuses on the Data Storage market size by players, by Type and by Application, for the period 2017-2026. The key players include the global and local players, which play important roles in United States.

This Data Storage Market Report offers analysis and insights based on original consultations with important players such as CEOs, Managers, and Department heads of suppliers, manufacturers, and distributors.

How much is the Data Storage market worth?

As a result of the Ukraine-Russia War and COVID-19 epidemic, the Data Storage market is estimated to be worth USD million in 2022 and is forecast to be worth USD million by 2026, with a CAGR estimated to generate a lot of revenue till 2026.

Get a demo Copy of the Data Storage Market Report 2022

The investigation report has solidified the examination of different factors that increment the market's turn of events. It lays out examples, limitations, and drivers that change the market in either a positive or negative manner. This part also gives the degree of different sections and applications that could influence the market from now into the foreseeable future. The point by point information relies upon most accurate things and essential accomplishments. This portion moreover gives an exploration of the volume of creation about the overall market and about each sort from 2017 to 2026.

What are the key companies covered in the Data Storage Market?

The Major Players covered in the Data Storage Market report are:

● HPE ● NetApp ● Dell EMC ● IBM ● Pure Storage ● Hitachi ● Fujitsu ● Huawei ● Western Digital

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A thorough evaluation of the controls associated with the report portrays the separation to drivers and gives space for essential arrangement. Factors that obscure the market advancement are critical as they can be seen to devise different bends for getting hold of the advantageous entryways that are accessible in the reliably creating business area. Besides, pieces of information into market capable's viewpoints have been taken to appreciate the market better.

Data Storage Market - Size, Shares, Scope, Competitive Landscape and Segmentation Analysis:

The report focuses on the Data Storage market size, segment size (mainly covering product type, application, and geography), competitor landscape, accurate status, and development trends. Furthermore, the report provides strategies for companies to overcome threats posed by COVID-19. Technological innovation and advancement will further optimize the performance of the product, enabling it to acquire a wider range of applications in the downstream market. Moreover, customer preference analysis, market dynamics (drivers, restraints, opportunities), new product release, impact of COVID-19, regional conflicts and carbon neutrality provide crucial information for us to take a deep dive into the Data Storage market.

The major players in the global Data Storage Market are summarized in a report to understand their role in the market and future strategies. Numerous marketing channels and strategies are likely to thrive during the forecast period and were also identified in reports that help readers develop a winning approach.

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What segments are covered in Data Storage Market report?

Data Storage Market is segmented on the basis of type, end-use industry and application. The growth amongst the different segments helps you in attaining the knowledge related to the different growth factors expected to be prevalent throughout the market and formulate different strategies to help identify core application areas and the difference in your target markets.

On the basis of Product Type, Data Storage Market is segmented into:
● All-Flash Arrays
● Hybrid Storage Arrays
● HDD Arrays

The report studies end-user applications in various product segments and the global Data Storage Market. By collecting important data from relevant sources, the report assesses the growth of individual market segments. In addition, the market size and growth rate of each segment is explained in the report. The report considers key geographic segments and describes all the favourable conditions driving market growth.

On the basis of the End Users / Applications, Data Storage Market is segmented into:
● IT and Telecom
● Healthcare
● Education
● Manufacturing
● Media and Entertainment
● Energy and Utility
● Retail and e-Commerce
● Others

The country section of the report also includes individual market influences affecting current and future market trends and changes in market regulation at the country level.

Get a demo Copy of the Data Storage Market Report 2022

On the basis of the Geography, Data Storage Market is segmented into:
- North America [US, Canada, Mexico]
- Europe [Germany, UK, France, Russia, Italy, Rest of Europe]
- Asia-Pacific [China, India, Japan, South Korea, Southeast Asia, Australia, Rest of Asia Pacific]
- South America [Brazil, Argentina, Rest of South America]
- Middle East and Africa [GCC, North Africa, South Africa, Rest of Middle East and Africa]

Through a comparative examination of the past and present scenarios, the Data Storage research offers a complete blueprint of the industry scenario across the assessment timeframe; assisting stakeholders in establishing action plans that guarantee maximum growth while managing market risks. Furthermore, the study document provides a complete review of the major industry segments to discover the best investment opportunities. It also examines all of the major market participants in terms of their financials, growth plans, and product and service offerings to provide a comprehensive picture of the competitive environment.

Data Storage Market - Impact of Covid-19 and Recovery Analysis:

We have been tracking the direct impact of COVID-19 on this market, as well as the indirect impact from other industries. This report analyses the impact of the pandemic on the Data Storage market from a Global and Regional perspective. The report outlines the market size, market characteristics, and market growth for Data Storage industry, categorized by type, application, and consumer sector. In addition, it provides a comprehensive analysis of aspects involved in market development before and after the Covid-19 pandemic. Report also conducted a PESTEL analysis in the industry to study key influencers and barriers to entry.

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Data Storage Market Drivers and Restrains:

The Data Storage industry research report provides an analysis of the various factors driving the markets growth. It creates trends, constraints and impulses that change the market in a positive or negative direction. This section also discusses the various segments and applications that could affect the future Data Storage market. Details are based on current trends and past achievements. The report includes a comprehensive boundary condition assessment that compares drivers and provides strategic planning. The factors that impede market growth are fundamental because they create different curves to seize opportunities in emerging markets. We also gather information from the opinions of market experts to better understand the market.

Years considered for this report:
- Historical Years:2017-2021
- Base Year:2021
- Estimated Year:2022
- Forecast Period:2022-2026

What the Report has to Offer?
- Market Size Estimates:The report offers accurate and reliable estimation of the market size in terms of value and volume. Aspects such as production, distribution and supply chain, and revenue for the Data Storage market are also highlighted in the report
- Analysis on Market Trends:In this part, upcoming market trends and development have been scrutinized
- Growth Opportunities:The report here provides clients with the detailed information on the lucrative opportunities in the Data Storage market
- Regional Analysis:In this section, the clients will find comprehensive analysis of the potential regions and countries in the Data Storage market
- Analysis on the Key Market Segments:The report focuses on the segments: end user, application, and product type and the key factors fuelling their growth.
- Vendor Landscape:Competitive landscape provided in the report will help the companies to become better equipped to be able to make effective business decisions.

Reasons to buy this report:
- To gain insightful analyses of the market and have comprehensive understanding of the global market and its commercial landscape.
- Assess the production processes, major issues, and solutions to mitigate the development risk.
- To understand the most affecting driving and restraining forces in the market and its impact in the global market.
- Learn about the market strategies that are being adopted by leading respective organizations.
- To understand the future outlook and prospects for the market.
- Besides the standard structure reports, we also provide custom research according to specific requirements.

Inquire more and share questions if any before the purchase on this

Data Storage Market - Table of Content (TOC):

1 Data Storage Market Overview
1.1 Product Overview and Scope of Data Storage Market
1.2 Data Storage Market Segment by Type
1.2.1 Global Data Storage Market Sales and CAGR Comparison by Type (2017-2026)
1.3 Global Data Storage Market Segment by Application
1.3.1 Data Storage Market Consumption (Sales) Comparison by Application (2017-2026)
1.4 Global Data Storage Market, Region Wise (2017-2026)
1.4.1 Global Data Storage Market Size (Revenue) and CAGR Comparison by Region (2017-2026)
1.4.2 United States Data Storage Market Status and Prospect (2017-2026)
1.4.3 Europe Data Storage Market Status and Prospect (2017-2026)
1.4.4 China Data Storage Market Status and Prospect (2017-2026)
1.4.5 Japan Data Storage Market Status and Prospect (2017-2026)
1.4.6 India Data Storage Market Status and Prospect (2017-2026)
1.4.7 Southeast Asia Data Storage Market Status and Prospect (2017-2026)
1.4.8 Latin America Data Storage Market Status and Prospect (2017-2026)
1.4.9 Middle East and Africa Data Storage Market Status and Prospect (2017-2026)
1.5 Global Market Size (Revenue) of Data Storage (2017-2026)
1.5.1 Global Data Storage Market Revenue Status and Outlook (2017-2026)
1.5.2 Global Data Storage Market Sales Status and Outlook (2017-2026)
1.6 Influence of Regional Conflicts on the Data Storage Industry
1.7 Impact of Carbon Neutrality on the Data Storage Industry

2 Data Storage Market Upstream and Downstream Analysis
2.1 Data Storage Industrial Chain Analysis
2.2 Key Raw Materials Suppliers and Price Analysis
2.3 Key Raw Materials Supply and Demand Analysis
2.4 Market Concentration Rate of Raw Materials
2.5 Manufacturing Process Analysis
2.6 Manufacturing Cost Structure Analysis
2.7 Major Downstream Buyers of Data Storage Analysis
2.8 Impact of COVID-19 on the Industry Upstream and Downstream

3 Players Profiles

4 Global Data Storage Market Landscape by Player
4.1 Global Data Storage Sales and Share by Player (2017-2022)
4.2 Global Data Storage Revenue and Market Share by Player (2017-2022)
4.3 Global Data Storage Average Price by Player (2017-2022)
4.4 Global Data Storage Gross Margin by Player (2017-2022)
4.5 Data Storage Market Competitive Situation and Trends
4.5.1 Data Storage Market Concentration Rate
4.5.2 Data Storage Market Share of Top 3 and Top 6 Players
4.5.3 Mergers and Acquisitions, Expansion

5 Global Data Storage Sales, Revenue, Price Trend by Type
5.1 Global Data Storage Sales and Market Share by Type (2017-2022)
5.2 Global Data Storage Revenue and Market Share by Type (2017-2022)
5.3 Global Data Storage Price by Type (2017-2022)
5.4 Global Data Storage Sales, Revenue and Growth Rate by Type (2017-2022)

6 Global Data Storage Market Analysis by Application
6.1 Global Data Storage Consumption and Market Share by Application (2017-2022)
6.2 Global Data Storage Consumption Revenue and Market Share by Application (2017-2022)
6.3 Global Data Storage Consumption and Growth Rate by Application (2017-2022)

7 Global Data Storage Sales and Revenue Region Wise (2017-2022)
7.1 Global Data Storage Sales and Market Share, Region Wise (2017-2022)
7.2 Global Data Storage Revenue and Market Share, Region Wise (2017-2022)
7.3 Global Data Storage Sales, Revenue, Price and Gross Margin (2017-2022)
7.4 United States Data Storage Sales, Revenue, Price and Gross Margin (2017-2022)
7.4.1 United States Data Storage Market Under COVID-19
7.5 Europe Data Storage Sales, Revenue, Price and Gross Margin (2017-2022)
7.5.1 Europe Data Storage Market Under COVID-19
7.6 China Data Storage Sales, Revenue, Price and Gross Margin (2017-2022)
7.6.1 China Data Storage Market Under COVID-19
7.7 Japan Data Storage Sales, Revenue, Price and Gross Margin (2017-2022)
7.7.1 Japan Data Storage Market Under COVID-19
7.8 India Data Storage Sales, Revenue, Price and Gross Margin (2017-2022)
7.8.1 India Data Storage Market Under COVID-19
7.9 Southeast Asia Data Storage Sales, Revenue, Price and Gross Margin (2017-2022)
7.9.1 Southeast Asia Data Storage Market Under COVID-19
7.10 Latin America Data Storage Sales, Revenue, Price and Gross Margin (2017-2022)
7.10.1 Latin America Data Storage Market Under COVID-19
7.11 Middle East and Africa Data Storage Sales, Revenue, Price and Gross Margin (2017-2022)
7.11.1 Middle East and Africa Data Storage Market Under COVID-19

8 Global Data Storage Market Forecast (2022-2026)
8.1 Global Data Storage Sales, Revenue Forecast (2022-2026)
8.1.1 Global Data Storage Sales and Growth Rate Forecast (2022-2026)
8.1.2 Global Data Storage Revenue and Growth Rate Forecast (2022-2026)
8.1.3 Global Data Storage Price and Trend Forecast (2022-2026)
8.2 Global Data Storage Sales and Revenue Forecast, Region Wise (2022-2026)
8.3 Global Data Storage Sales, Revenue and Price Forecast by Type (2022-2026)
8.4 Global Data Storage Consumption Forecast by Application (2022-2026)
8.5 Data Storage Market Forecast Under COVID-19

9 Industry Outlook
9.1 Data Storage Market Drivers Analysis
9.2 Data Storage Market Restraints and Challenges
9.3 Data Storage Market Opportunities Analysis
9.4 Emerging Market Trends
9.5 Data Storage Industry Technology Status and Trends
9.6 News of Product Release
9.7 Consumer Preference Analysis
9.8 Data Storage Industry Development Trends under COVID-19 Outbreak
9.8.1 Global COVID-19 Status Overview
9.8.2 Influence of COVID-19 Outbreak on Data Storage Industry Development

10 Research Findings and Conclusion

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Data Storage Market - Research Methodology:

The key research methodology is data triangulation which involves data processing, analysis of the impact of knowledge variables on the market, and first (industry expert) validation. Data collection and base year analysis is completed using data collection modules with large demo sizes. The market data is analyzed and forecasted using market statistical and coherent models. Also market share analysis and key analysis are the main success factors within the market report.

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