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Exam Code: COF-C02 Practice exam 2023 by team
COF-C02 SnowPro Core Certification

Exam Details for COF-C02 SnowPro Core Certification:

Number of Questions: The exam consists of approximately 65 multiple-choice and multiple-select questions.

Time Limit: The total time allocated for the exam is 90 minutes (1 hour and 30 minutes).

Passing Score: To pass the exam, you must achieve a minimum score of 70%.

Exam Format: The exam is conducted online and is proctored. You will be required to answer the questions within the allocated time frame.

Course Outline:

1. Snowflake Architecture and Deployment Considerations:
- Understand the architecture and components of Snowflake
- Learn about Snowflake deployment options and considerations
- Familiarize with Snowflake security and access control mechanisms

2. Snowflake Account and User Management:
- Create and manage Snowflake accounts and users
- Configure user roles and privileges in Snowflake
- Implement authentication and authorization mechanisms

3. Snowflake Data Loading and Unloading:
- Load data into Snowflake using different methods (e.g., COPY, INSERT)
- Unload data from Snowflake to external storage or cloud services
- Optimize data loading and unloading processes in Snowflake

4. Snowflake Querying and Optimization:
- Write SQL queries in Snowflake to retrieve and manipulate data
- Understand query optimization techniques in Snowflake
- Utilize Snowflake query monitoring and troubleshooting tools

5. Snowflake Data Modeling and Warehousing:
- Design and create Snowflake databases and schemas
- Develop and implement Snowflake data models
- Optimize Snowflake data warehousing performance

6. Snowflake Security and Data Protection:
- Implement data security controls in Snowflake
- Configure Snowflake encryption and data masking
- Ensure compliance with data protection regulations

7. Snowflake Clustering and Partitioning:
- Understand Snowflake clustering and partitioning concepts
- Optimize data storage and retrieval using clustering and partitioning
- Design and implement effective clustering and partitioning strategies

8. Snowflake Performance and Scalability:
- Monitor and optimize Snowflake performance and scalability
- Configure Snowflake virtual warehouses for workload management
- Implement Snowflake resource monitoring and allocation

Exam Objectives:

1. Understand the architecture and deployment considerations of Snowflake.
2. Manage Snowflake accounts and user access.
3. Load and unload data from Snowflake using various methods.
4. Write and optimize SQL queries in Snowflake.
5. Design and implement data models in Snowflake.
6. Implement security controls and data protection measures in Snowflake.
7. Utilize clustering and partitioning techniques for performance optimization.
8. Monitor and optimize Snowflake performance and scalability.

Exam Syllabus:

The exam syllabus covers the following topics:

1. Snowflake Architecture and Deployment Considerations
- Snowflake architecture and components
- Snowflake deployment options and considerations
- Snowflake security and access control mechanisms

2. Snowflake Account and User Management
- Snowflake account and user creation and management
- User roles and privileges configuration
- Authentication and authorization mechanisms in Snowflake

3. Snowflake Data Loading and Unloading
- Data loading methods in Snowflake (e.g., COPY, INSERT)
- Data unloading from Snowflake to external storage or cloud services
- Data loading and unloading process optimization in Snowflake

4. Snowflake Querying and Optimization
- SQL query writing and execution in Snowflake
- Query optimization techniques in Snowflake
- Snowflake query monitoring and troubleshooting tools

5. Snowflake Data Modeling and Warehousing
- Snowflake database and schema design and creation
- Snowflake data model development and implementation

- Data warehousing performance optimization in Snowflake

6. Snowflake Security and Data Protection
- Data security controls implementation in Snowflake
- Snowflake encryption and data masking configuration
- Data protection regulation compliance in Snowflake

7. Snowflake Clustering and Partitioning
- Snowflake clustering and partitioning concepts
- Data storage and retrieval optimization using clustering and partitioning
- Effective clustering and partitioning strategies in Snowflake

8. Snowflake Performance and Scalability
- Snowflake performance and scalability monitoring and optimization
- Virtual warehouse configuration for workload management
- Snowflake resource monitoring and allocation

SnowPro Core Certification
SnowFlake Certification mission
Killexams : SnowFlake Certification mission - BingNews Search results Killexams : SnowFlake Certification mission - BingNews Killexams : Snowflake beats expectations and maintains full-year guidance, sending its stock higher

Shares of the cloud data warehouse firm Snowflake Inc. moved higher in extended trading today after the company beat expectations for its second-quarter earnings and kept its full-year revenue forecast steady.

On the other hand, Snowflake’s guidance for the current quarter came up just short of Wall Street’s targets. Perhaps as a result, its stock gained a relatively modest 4% in after-hours trading, adding to a gain of 2% during the regular trading session.

The company reported earnings before certain costs such as stock compensation of 22 cents per share, well ahead of Wall Street’s forecast of just 10 cents per share. Revenue rose 36% to $674 million, beating the consensus estimate of $662 million. Product revenue also surpassed expectations, coming to $640.2 million, ahead of the $626 million target.

Snowflake sells cloud data warehouse services that are popular with enterprises. Its platform is used by customers to store, process and consolidate data that can be used to derive business insights and train artificial intelligence models.

According to Snowflake Chief Executive Frank Slootman (pictured), the company is poised to benefit from the technology industry’s exact AI boom. The incredible popularity of generative AI chatbots such as ChatGPT has driven huge interest in the technology, and these days many companies are scrambling to see how they can use it to Improve various business processes.

Slootman said Snowflake is a useful partner for AI initiatives because it enables models to be trained on highly curated and optimized data. “Snowflake as the global epicenter of trusted enterprise data is well positioned to enable the growing interest in AI/ML,” Slootman said. “Enterprises and institutions alike are increasingly aware they cannot have an AI strategy without a data strategy.”

During the quarter just gone, Snowflake held its annual Snowflake Summit user conference. At the show Slootman stopped by SiliconANGLE Media’s mobile livestreaming studio theCUBE, where he laid out the company’s strategy for AI using the vast amounts of data it manages and hosts for its customers. Those plans include a close collaboration with Nvidia Corp., whose graphics processing units are the most widely used chip technology for training AI models.

“They felt that it was a very convincing strategy, very compelling strategy,” Slootman said, talking about Snowflake’s investor’s reaction to the company’s AI plans. “The conference content helps them understand the vastness of the strategy and how far we’ve come.”

In a conference call with analysts, Snowflake Chief Financial Officer Mike Scarpelli was asked when software companies might expect to see revenue increase as a result of this focus on AI. They noted that Nvidia has already seen enormous growth.

However, Scarpelli said investors will have to be a bit more patient. Most likely, the real impact will be felt next year, he said. One problem is that many enterprises face a long wait to get their hands on Nvidia’s GPUs, since they’re in such high demand.

Although Snowflake predicted a slight drop in revenue growth for the third quarter, it maintained its full-year sales forecast. Three months prior, it dropped its fiscal revenue forecast, spooking investors and sending its stock down more than 16%.

Snowflake said it sees product revenue of between $670 million and $675 million for the third quarter, the midpoint of which is below analysts’ consensus target of $675 million. For the full year, though, it maintained a forecast of $2.6 billion in product revenue. That’s still below Wall Street’s fiscal 2024 target of $2.76 billion, but investors were clearly relieved it hasn’t gotten any worse.

Scarpelli said the company is maintaining its full-year forecast because it isn’t seeing customers reduce consumption any further. “We are seeing encouraging signs of stabilization, but not a recovery,” he said.

Here’s Slootman’s full interview on theCUBE, where he also discusses Snowflake’s expansion into new markets and its efforts to grow its developer ecosystem through its native apps framework:

Photo: SiliconANGLE

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Wed, 23 Aug 2023 13:32:00 -0500 en-US text/html
Killexams : Snowflake Now Wants You to Converse With Your Data

Snowflake’s growth trajectory has been nothing short of remarkable. Since 2012, the company has witnessed exponential market adoption and has attracted a diverse range of clients, from startups to Fortune 500 giants. Some of its notable customers include Adobe, Airbnb, BlackRock, Dropbox, Pepsico, ConAgra Foods, Novartis and Yamaha. In India, Snowflake caters to the needs of companies such as Porter, Swiggy and Urban Company. The rapid expansion is a testament to Snowflake’s ability to address the ever-increasing demands of the data-driven world we live in.

But today, we are stepping into the age of generative AI and Snowflake too is gearing up to bring the best of the technology to its long list of customers. Torsten Grabs, senior director of product management at Snowflake told AIM that with the advent of generative AI, we will increasingly see less technical users successfully interact with computers with technology and that’s probably the broadest and biggest impact that he would expect from generative AI and Large Language Models (LLMs) across the board. Moreover, talking about the impact of generative AI on Snowflake, he said that it has impacted Snowflake on two distinct levels.

Firstly, like almost every other company, generative AI is leading to productivity improvements at Snowflake. Grabs anticipates developers working on Snowflake to benefit the most from generative AI. This concept is akin to Microsoft’s co-pilot and AWS’s CodeWhisperer, where a coding assistant aids in productivity by comprehending natural language and engaging in interactive conversations to facilitate faster and more precise code creation.

Moreover, Snowflake is harnessing generative AI to enhance conversational search capabilities. For instance, when accessing the Snowflake marketplace, it employs conversational methods to identify suitable datasets that address your business needs effectively. “There’s another layer that I think is actually very critical for everybody in the data space, which is around applying LLMs to the data that’s being stored or managed in a system like Snowflake,” Grabs said. The big opportunity for Snowflake lies in leveraging generative AI to offer enhanced insights into the data managed and stored within these systems. 

Conversing with your data 

On May 24, 2023, Snowflake acquired Neeva AI with the aim of accelerating search capabilities within Snowflake’s Data Cloud platform by leveraging Neeva’s expertise in generative AI-based search technology. “We recognised the necessity of integrating robust search functionality directly into Snowflake, making it an inherent and valuable capability. Partnering with Neeva AI further enriched our approach, combining their expertise in advanced search with generative AI, benefiting us in multiple dimensions,” Grabs said.

Grabs believes the Neeva AI acquisition is going to bring a host of benefits to Snowflake’s customers. Most importantly, it will give them the ability to talk to their data essentially in a conversational way. “It’s analogous to the demonstration we presented, where a conversation with the marketplace utilizes metadata processed by the large language model to discover relevant datasets,” Grabs said.

Now consider scaling this process and going beyond metadata, involving proprietary sensitive data. By employing generative AI, Snowflake’s customers can engage in natural language conversations to gain precise insights about their enterprise’s data.

Building LLMs for customers

Building on generative AI capabilities, Snowflake, at its annual user conference called ‘Snowflake Summit 2023’ also announced a new LLM built from Applica’s generative AI technology to help customers understand documents and put their unstructured data to work. “We have specifically built this model for document understanding use cases and we started with TILT base model that we leveraged and then built on top of it,” Grabs said.

When compared to the GPT models from OpenAI or other models developed by labs such as Antrhopic, Snowflake’s LLMs offers few distinct advantages. For example, the GPT models are trained on the entirety of publicly available internet data, resulting in broad capabilities but high resource demands. Their resource-intensive nature also makes them costly to operate. Much of these resources are allocated to aspects irrelevant to your specific use case. Grabs believes utilising a more tailored, specialised model designed for your specific use case allows for a narrower model with a reduced resource footprint, leading to increased cost-effectiveness.

“This approach is also poised to yield significantly superior outcomes due to its tailor-made design for the intended use case. Furthermore, the model can be refined and optimised using your proprietary data. This principle isn’t confined solely to the document AI scenarios; rather, it’s a pattern that will likely extend more widely across various use cases.”

In many instances, these specialised models are expected to surpass broad foundational models in both accuracy and result quality. Additionally, they are likely to prove more resource-efficient and cost-effective to operate. “Our document AI significantly aids financial institutions in automating approval processes, particularly for mortgages. Documents are loaded into the system, the model identifies document types (e.g., salary statements), extracts structured data, and suggests approvals. An associate reviews and finalises decisions, streamlining the process and enhancing efficiency.”

Addressing customer’s concerns

While generative AI has garnered significant interest, enterprises, including Snowflake’s clients, which encompasses 590 Forbes Global 2000 companies, remain concerned about the potential risks tied to its utilisation. “I think some of the top concerns for pretty much all of the customers that I’m talking to is around security, privacy, data governance and compliance,” Grab said.

This presents a significant challenge, especially concerning advanced commercial LLMs. These models are often hosted in proprietary cloud services that require interaction. For enterprise clients with sensitive data containing personally identifiable information (PII), the prospect of sending such data to an external system outside their control and unfamiliar with their cybersecurity processes raises concerns. This limitation hinders the variety of data that can interact with such systems and services. 

“Our long-standing stance has been to avoid dispersing data across various locations within the data stack or across the cloud. Instead, we advocate for bringing computation to the data’s location, which is now feasible with the abundant availability of compute resources,” Grabs said. Unlike a decade or two ago when compute was scarce, the approach now is to keep data secure and well-governed in its place and then bring computation to wherever the data resides. 

He believes this argument extends to generative AI and LLMs as well. “We would like to offer the state-of-the-art LLMs and side by side the compelling open-source options that operate within the secure confines of the customer’s Snowflake account. This approach ensures that the customer’s proprietary or sensitive data remains within the security boundary of their Snowflake account, offering them peace of mind.”

Moreover, on the flip side, another crucial aspect to consider is the protection of proprietary intellectual property (IP) within commercial LLMs. The model’s code, weights, and parameters often involve sensitive proprietary information. “With our security model integrated into native apps on the marketplace, we can ensure that commercial LLM vendors’ valuable IP remains undisclosed to customers utilising these models within their Snowflake account. Our role in facilitating the compute for both parties empowers us to maintain robust security and privacy boundaries among all participants involved in the process,” Grabs concluded. 

Sun, 13 Aug 2023 23:56:00 -0500 en-US text/html
Killexams : Our Mission

C-SPAN is a public service created by the American Cable Television Industry

  • To provide C-SPAN's audience access to the live gavel-to-gavel proceedings of the U.S. House of Representatives and the U.S. Senate, and to other forums where public policy is discussed, debated and decided––all without editing, commentary or analysis and with a balanced presentation of points of view;
  • To provide elected and appointed officials and others who would influence public policy a direct conduit to the audience without filtering or otherwise distorting their points of view;
  • To provide the audience, through the call-in program, direct access to elected officials, other decision makers and journalists on a frequent and open basis;
  • To employ production values that accurately convey the business of government rather than distract from it; and
  • To conduct all other aspects of its operations consistent with these principles.
Tue, 18 Aug 2020 14:52:00 -0500 en-us text/html
Killexams : Informatica and Snowflake partner for intelligent data management

Even though enterprise data sources, such as resource planning and customer relationship management systems are critical for analytics, retrieving data from them is a tall order.

As innovations continue to rock the data space, Informatica SuperPipe for Snowflake was devised to get mission-critical data out of hard-to-get places at a 3.5 times faster replication and ingestion rate, according to Rik Tamm-Daniels (pictured), general vice president of ecosystem alliances and technology at Informatica Inc.

“One of the big ones you mentioned is SuperPipe for Snowflake, and we think about the different types of needs for data integration,” he stated. “Reducing the latency of data, making it more real-time, that’s what SuperPipe’s all about. We see up to about three and a half times faster performance than our previous kind of change data capture replication technology. It’s a huge leap forward, leveraging some of the latest Snowpipe streaming capabilities from Snowflake.”

Tamm-Daniels spoke with theCUBE industry analysts Lisa Martin and Dave Vellante at Snowflake Summit, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how Informatica has partnered with Snowflake Inc. to enhance the intelligent data management sector. (* Disclosure below.)

Revolutionizing data management using AI

As generative artificial intelligence and large language models – think ChatGPT – continue to gain steam, Informatica seeks to revamp the data management sector using AI. This can be illustrated by the fact that the company recently rolled out Claire GPT and extended its Claire copilot capabilities, according to Tamm-Daniels.

“When you think about the LLM space, there are really two angles for us in generative AI,” he noted. “The first is those models need data … we’re also invested heavily in using generative AI to really revolutionize data management, and so we announced our Claire GPT and Claire Copilot at Informatica World back in early May to address those kinds of opportunities.”

By incorporating generative AI into the data management cloud interface, Informatica is able to turn metrics into a pipeline of integration and connections. This is highly transformative because a text box offers more options, Tamm-Daniels pointed out.

“Claire GPT, the idea is I think one of the big transformative things about generative AI is it actually lets you take some very complex and nuanced requests, express them in pretty significant descriptive English language descriptions, and then actually turn them into something actionable, executable,” he noted. “On the Claire copilot, that’s all about … how do we bring the power of generative AI to help make better decisions, to help have assistive technology, to recommend data quality transformations or items that you be concerned about.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Snowflake Summit:

(* Disclosure: Snowflake Inc. and Informatica Inc. sponsored this segment of theCUBE. Neither Snowflake, Informatica, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE

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Tue, 25 Jul 2023 04:09:00 -0500 en-US text/html
Killexams : Apply for Certification Today!

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Mon, 31 Jul 2023 21:26:00 -0500 en-US text/html
Killexams : Snowflake: Why The Partnership With Microsoft Is A Game Changer
Forex diagrams and stock market rising lines with numbers


Snowflake (NYSE:SNOW) plunged after its most exact earnings release in spite of strong results, but has since recovered its losses. Wall Street appears concerned about management's conservative guidance, which calls for a sizable slowdown in the company’s growth rate. Customers are adapting to

Thu, 27 Jul 2023 09:48:00 -0500 en text/html Killexams : Mission and Values

Mission: Advancing Health Worldwide

UC San Francisco is the leading university dedicated to advancing health worldwide through preeminent biomedical research, graduate-level education in the life sciences and health professions, and excellence in patient care.

Within our overarching advancing health worldwide mission, UCSF is devoted at every level to serving the public.

UCSF’s commitment to public service dates to the founding of its predecessor institution, Toland Medical College, in 1864. Born out of the overcrowded and unsanitary conditions of Gold Rush-era San Francisco, Toland Medical College trained doctors to elevate the standards of public health in the burgeoning city.

By 1873, the University of California acquired the college and forged a partnership with San Francisco General Hospital that continues to this day and serves as a model for delivering leading-edge care at a public safety-net hospital.

Today UCSF’s public mission goes beyond San Francisco and delivers a substantial impact on a national and global level by innovating health care approaches for the world’s most vulnerable populations, training the next generation of doctors, nurses, dentists, pharmacists and scientists; supporting elementary and high school education; and translating scientific discoveries into better health for everyone.


In his 2016 State of the University Address, Chancellor Sam Hawgood announced that UCSF is embracing a common set of values to set a clear direction for all members of the UCSF community as we work together to fulfill our mission. This set of overarching values aligns with UCSF’s Principles of Community and Code of Ethics.

PRIDE values are:

Professionalism: To be competent, accountable, reliable and responsible, interacting positively and collaboratively with all colleagues, students, patients, visitors and business partners.

Respect: To treat all others as you wish to be treated, being courteous, kind and acting with utmost consideration for others.

Integrity: To be honest, trustworthy and ethical, always doing the right thing, without compromising the truth, and being fair and sincere.

Diversity: To appreciate and celebrate differences in others, creating an environment of equity and inclusion with opportunities for everyone to reach their potential.

Excellence: To be dedicated, motivated, innovative and confident, giving your best every day, encouraging and supporting others to excel in everything they do.

Fri, 01 Apr 2022 06:47:00 -0500 en text/html
Killexams : Azure Synapse Analytics vs Snowflake: ETL Tool Comparison

Azure Synapse Analytics and Snowflake are two commonly recommended ETL tools for businesses that need to process large amounts of data. Choosing between the two will depend on the unique strengths of these services and your company’s needs. These are the key differences between Synapse and Snowflake, including their features and where they excel.

Jump to:

What is Azure Synapse Analytics?

Azure Synapse Analytics logo
Image: Azure Synapse

Azure Synapse Analytics (formerly known as Azure SQL Data Warehouse) is a data analytics service from Microsoft. It’s part of the Azure platform, which includes products like Azure Databricks, Cosmos DB and Power BI.

Microsoft describes it as offering a “… unified experience to ingest, explore, prepare, transform, manage, and serve data for immediate BI and machine learning needs.” The service is one of the most popular tools available for information warehousing and the management of big data systems.

Key features of Azure Synapse Analytics include:

  • End-to-end cloud data warehousing.
  • Built-in governance tools.
  • Massively parallel processing.
  • Seamless integration with other Azure products.

What is Snowflake?

Snowflake logo in blue on a white background
Image: Snowflake

Snowflake is another popular big data platform, developed by a company of the same name. It’s a fully managed platform as a service used for various applications — including data warehousing, lake management, data science and secure sharing of real-time information.

A Snowflake data warehouse is built on either the Amazon Web Services or Microsoft Azure cloud infrastructure. Cloud storage and compute power can scale independently.

Like most available data platforms, Snowflake is built with key trends in business intelligence automation in mind, including automation, segmentation of intelligence workflows and growing use of anything as a service tools.

The major competitors of Snowflake include Dremio, Firebolt, and Palantir.

Key features of Snowflake’s platform include:

  • Scalable computing.
  • Data sharing.
  • Data cloning.
  • Integration with third-party tools, including many Azure products.

SEE: For more information, explore our overview of Snowflake.

Azure Synapse Analytics vs. Snowflake: Comparison table

Features Azure Synapse Analytics Snowflake
Scalability Excellent Excellent
Control over infrastructure Yes Limited
Integration with Azure Yes No
Built-in security features Yes Yes
Cloud-native No Yes
Ease of use Limited Yes
Real-time and streaming data processing Yes Yes

Azure Synapse Analytics and Snowflake pricing

Azure Synapse Analytics pricing

Azure Snapase offers different pricing tiers and categories based on region, type of service, storage, unit of time and other factors. The prepurchase plans are available in six tiers starting with 5,000 Synapse Commit Units for $4,750. The higher tier is priced at $259,200 for 260,000 SCUs.

The pricing for data integration capabilities offered by Azure Synapse Analytics is based on data pipeline activities, integration runtime hours, operation charges, and data flow cluster size and execution. Each activity has separate charges. For example, Basis Data Flows are charged at $0.257 per vCore-hour, while Standard Data Flows are charged at $0.325 per vCore-hour.

Snowflake pricing

The pricing for Snowflake is divided into four tiers, the pricing of which depends on the preferred platform and region. For example, if you prefer the Microsoft Azure platform and are located in the U.S. West region, you will pay the following:

  • Standard: $2 per credit.
  • Enterprise: $3 per credit.
  • Business Critical: $4 per credit.
  • AVS: Customized pricing.

You can choose to pay an extra $50 per terabyte per month for on-demand storage or $23 per terabyte per month for capacity storage.

Feature comparison: Azure Synapse Analytics vs. Snowflake

The two extract, transfer and load products have a lot in common, but they differ in specific features, strengths, weaknesses and popular use cases.

Use cases and versatility

Synapse Analytics and Snowflake are built for a range of data analysis and storage applications, but Snowflake is a better fit for conventional business intelligence and analytics. It includes near-zero maintenance with features like automatic clustering and performance optimization tools.

Businesses that use Snowflake for storage and analysis may not need a full-time administrator who has deep experience with the platform.

By comparison, native integration with Spark Pool and Delta Lake makes Synapse Analytics an excellent choice for advanced big data applications, including artificial intelligence, machine learning and data streaming. However, the platform will require much more labor and attention from analytics teams.

A Synapse Analytics administrator who is familiar with the platform and knows how to effectively manage the service will likely be necessary for a business to benefit fully. Setup of the Synapse Analytics platform will also likely be more involved, meaning businesses may need to wait longer to see results.


Snowflake isn’t built to run on a specific architecture and will run on top of three major cloud platforms: AWS, Microsoft Azure’s Cloud platform and Google Cloud. A layer of abstraction separates the Snowflake storage and compute credits from the genuine cloud resources from a business’s provider of choice.

Each virtual Snowflake warehouse has its own independent compute cluster. They don’t share resources, so the performance of one warehouse shouldn’t impact the performance of another.

Comparatively, Azure Synapse Analytics is built specifically for Azure Cloud. It’s designed from the ground up for integration with other Azure services. Snowflake will also integrate with many of these services, but it lacks some of the capabilities that make Synapse Analytics’ integration with Azure so seamless.


Snowflake has built-in auto-scaling capabilities and an auto-suspend feature that will allow administrators to dynamically manage warehouse resources as their needs change. It uses a per-second billing model, and being able to quickly scale storage and compute up or down can provide immediate cost savings.

The zero-copy cloning feature from Snowflake allows administrators to create a copy of tables, schemas and warehouses without duplicating the genuine data. This allows for even greater scalability.

Azure offers strong scalability but lacks some of the features that make Snowflake so flexible. Serverless SQL Pools and Spark Pools in Azure have automatic scaling by default. However, Dedicated SQL Pools require manual scaling.

SEE: Compare features of top time tracking software.

Azure Synapse Analytics pros and cons

Pros of Azure Synapse Analytics

  • Deep integration with the Azure ecosystem.
  • Unified platform for data warehousing and analytics.
  • Advanced analytics capabilities.

Cons of Azure Synapse Analytics

  • Steep learning curve for beginners.
  • Serverless capabilities are limited to newer Azure services.

Snowflake pros and cons

Pros of Snowflake

  • Cloud-native.
  • Automatic performance tuning.
  • User-friendly interface.

Cons of Snowflake

  • Limited control over the infrastructure.
  • Reliant on cloud service for availability.

Review methodology

To review Azure Synapse Analytics and Snowflake, we analyzed various factors, including the core functionality, scalability, ease of use, integration capabilities, security tools and customer support. We also analyzed the pricing structure of each solution, including its licensing costs and any extra charges for add-on services.

Should your organization use Azure Synapse Analytics or Snowflake?

A company deciding between Synapse and Snowflake is in a good position. Both platforms are excellent data storage and analysis services, with features necessary for many business intelligence and analysis workflows.

However, the two do differ when it comes to specific strengths and ideal use cases. Snowflake excels for companies that want to perform more traditional business intelligence analytics and will benefit from excellent scalability.

With Snowflake, you get a more user-friendly interface but are dependent on cloud service availability. As Snowflake is cloud-native, you also have limited direct control over the infrastructure. Businesses that need granular control over their infrastructure optimization will find this a key disadvantage of Snowflake.

Azure Synapse Analytics has a steeper learning curve than Snowflake, and scalability may be more challenging, depending on the type of pool a business uses. However, it’s an excellent choice for companies working with AI, ML and data streaming and will likely perform better than Snowflake for these applications.

Thu, 27 Jul 2023 05:40:00 -0500 en-US text/html
Killexams : Snowflake Inc.

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Markets Diary: Data on U.S. Overview page represent trading in all U.S. markets and updates until 8 p.m. See Closing Diaries table for 4 p.m. closing data. Sources: FactSet, Dow Jones

Stock Movers: Gainers, decliners and most actives market activity tables are a combination of NYSE, Nasdaq, NYSE American and NYSE Arca listings. Sources: FactSet, Dow Jones

ETF Movers: Includes ETFs & ETNs with volume of at least 50,000. Sources: FactSet, Dow Jones

Bonds: Bond quotes are updated in real-time. Sources: FactSet, Tullett Prebon

Currencies: Currency quotes are updated in real-time. Sources: FactSet, Tullett Prebon

Commodities & Futures: Futures prices are delayed at least 10 minutes as per exchange requirements. Change value during the period between open outcry settle and the commencement of the next day's trading is calculated as the difference between the last trade and the prior day's settle. Change value during other periods is calculated as the difference between the last trade and the most exact settle. Source: FactSet

Data are provided 'as is' for informational purposes only and are not intended for trading purposes. FactSet (a) does not make any express or implied warranties of any kind regarding the data, including, without limitation, any warranty of merchantability or fitness for a particular purpose or use; and (b) shall not be liable for any errors, incompleteness, interruption or delay, action taken in reliance on any data, or for any damages resulting therefrom. Data may be intentionally delayed pursuant to provider requirements.

Mutual Funds & ETFs: All of the mutual fund and ETF information contained in this display, with the exception of the current price and price history, was supplied by Lipper, A Refinitiv Company, subject to the following: Copyright 2019© Refinitiv. All rights reserved. Any copying, republication or redistribution of Lipper content, including by caching, framing or similar means, is expressly prohibited without the prior written consent of Lipper. Lipper shall not be liable for any errors or delays in the content, or for any actions taken in reliance thereon.

Cryptocurrencies: Cryptocurrency quotes are updated in real-time. Sources: CoinDesk (Bitcoin), Kraken (all other cryptocurrencies)

Calendars and Economy: 'Actual' numbers are added to the table after economic reports are released. Source: Kantar Media

Mon, 08 Dec 2014 17:27:00 -0600 en text/html
Killexams : Databricks vs Snowflake (2023): ETL Tool Comparison

With more and more solutions entering the enterprise software market, organizations have used many data sources for their operational processes. To properly transfer and share your organizational data and information between software systems, using an effective ETL solution is a necessity.

This resource will analyze two of the top ETL tools, Databricks and Snowflake, so you can see which would better satisfy your data extraction, transformation and loading needs.

Jump to:

What is Databricks?

Databricks ETL is a data and AI solution that organizations can use to accelerate the performance and functionality of ETL pipelines. The tool can be used in various industries and provides data management, security and governance capabilities.

What is Snowflake?

Snowflake is software that provides users with a data lake and warehousing environment for their data processing, unification and transformation. It is designed to simplify complex data pipelines and can be used with other data integration tools for greater functionality.

Databricks vs Snowflake: Comparison table

Features Databricks Snowflake
Focus on data warehousing No Yes
Cloud-native Yes Yes
Robust visualizations Yes Yes
Real-time data analytics Yes No
Built-in machine learning Yes No
Learn more Visit Databricks Visit Snowflake

Databricks and Snowflake pricing

After a free trial, Databricks can be purchased as a pay-as-you-go solution, with pricing based on computer usage. Alternatively, customers can purchase the software through a committed use plan. This means that users can commit to certain levels of usage and gain discounts when purchasing the software.

Snowflake offers similar pricing models for its software. The Data Cloud service can be purchased through a pay-as-you-go model that is usage-based with no long-term commitment, or through Snowflake On Demand. This lets customers access Snowflake by choosing pre-purchased software capacity options and promises discounts on the software’s overall cost.

Databricks vs. Snowflake feature comparison

Integration and synchronization

The Databricks solution allows users to make full use of their data by eliminating the silos that can complicate data. Data silos traditionally separate data engineering, analytics, BI, data science and machine learning. Companies can avoid proprietary walled gardens and other restrictions by removing these silos and allowing users to access and manage their structured and unstructured data through the Databricks platform. Users simply sync their data through a Databricks Data Lake connection for full access and automatic data update capabilities.

Snowflake supports data transformation both during loading and after it is loaded into the platform environment. The software has integration with many popular tools and solutions for easy data extraction and transformation into the target database through native connectivity with Snowflake. Snowflake takes care of multiple integration operations, including the preparation, migration, movement and management of data. In addition, the system provides capabilities for data loading from external and internal file locations, bulk loading, continuous loading and other data loading options (Figure A).

Figure A

Snowflake dashboard for S3 compatible storage providers
Snowflake can connect to S3 compatible storage providers to import data into a Snowflake internal stage. Image: Snowflake

Data visualization

Databricks gives users multiple methods for visualizing their data, including choropleth maps, marker maps, heatmaps, counters, pivot tables, charts, cohorts, markers, funnels, box plots, sunbursts, sankeys and word clouds. Once users store their data within their Databricks SQL data lake, they can create and save visualizations of their stored data (Figure B). Users can then edit, clone, customize or aggregate their visualizations. When they are happy with their visualizations, users can obtain them as image files or add them to their platform dashboards.

Figure B

Databricks data visualization for stored data
Databricks provides options for data visualization for users’ stored data. Image: Databricks

With the Snowflake web interface, Snowsight, users can visualize their data and query results as charts. Snowsight supports bar charts, line charts, scorecards, scatterplots and heat grids. Users can configure their data visualizations by adjusting their chart columns, column attributes and chart appearance. For example, to view data from specific time periods, users can select the buckets of time in the inspector panel to adjust the display without needing to modify their query. In addition, aggregation functions allow the system to determine single values from data points in a chart, and users can obtain their charts as .png files.

Data analysis

The Databricks SQL analytics platform uses machine learning to allow users to create queries in ANSI SQL and develop visualizations and dashboards using their accessible data. The visualizations allow users to gain insights and lightweight reporting from their data lake. However, users may prefer to utilize their existing third-party BI tools by connecting them to the platform. Tools like Microsoft PowerBI or Tableau can be used for analysis and reporting directly on the Databricks data lake.

Snowflake delivers insights on data through the Snowflake Data Cloud, a data platform that can be deployed across AWS, Google and Azure. It can analyze the data for various purposes: Data Engineering, Data Science, Data Lake, Applications, and Data Sharing and Exchange. Its visualization tools can enable users to gain valuable insight and information from their data through queries (Figure C). Additionally, Snowflake can be used together with other software systems for a broader range of analysis capabilities.

Figure C

Snowflake query interface
Snowflake users can query their data in the Snowflake interface. Image: Snowflake

Databricks pros and cons

Pros of Databricks

  • Built-in machine-learning capabilities.
  • Helpful online guides for utilizing and navigating the software.
  • Support for R, Java and Python.

Cons of Databricks

  • Steep learning curve for new users.
  • Challenging initial installation.

Snowflake pros and cons

Pros of Snowflake

  • Superb for data warehousing needs.
  • User-friendly interface with automatic performance scaling.
  • Useful integrations for extending the functionality of Snowflake’s software.

Cons of Snowflake

  • No built-in support for machine learning.
  • Provides limited control over the infrastructure.

Review methodology

This is a technical review using compiled literature researched from relevant databases. The information provided within this article is gathered from vendor websites or based on an aggregate of user feedback to ensure a high-quality review.

Should your organization use Databricks or Snowflake?

So which ETL solution is better for your organization? The best method to determine the ideal software solution for any purpose is to first identify your organization’s relevant aspects and requirements.

For example, if you require a cloud-based system for its data processing, utilizing Snowflake Data Cloud can enable your team to transform and manage its data through the online interface.

However, if your organization wishes to use its ETL solution to process big data batches, Databricks may be the better option. This is because Databricks has many functions and integrations for processing and analyzing big data sets.

Other factors to consider are the third-party products you want to use with your ETL solution. Ensure that the solution you choose has integration capabilities for each of your existing tools so that you can gain value from each of your data sources. Through thorough consideration of your organization’s needs, you can determine the best ETL solution to support your data operations.

Mon, 31 Jul 2023 08:19:00 -0500 en-US text/html
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