Written by Nick Ackerman, co-produced by Stanford Chemist. This article was originally published to members of the CEF/ETF Income Laboratory on July 22nd, 2022.
BlackRock Science and Technology Trust (NYSE:BST) and BlackRock Science and Technology Trust II (NYSE:BSTZ) are two funds that I regularly follow. I own both, and I've covered more of the basics of the funds in a previous article. I won't cover that whole syllabu again. Instead, today we'll look at more of an update on how these funds have been doing.
One of the biggest differences was that BST had traditionally invested in mostly larger, more well-established companies.. BSTZ had more of a tilt toward speculative and private names. This difference has become less pronounced as they moved BST into more speculative names as well. Still, several of the large mega-cap names remain. That hasn't provided too much solace to the fund, though, as it has been experiencing strong losses regardless.
Here is a look at the performance of the price and NAV of the funds since BSTZ's launch. This chart does not show the impacts of the distribution (i.e., it is NOT showing total returns.) The correlation metric is also provided, showing a significant correlation.
This differs from what we exhibited in BlackRock Health Sciences Trust (BME) and BlackRock Health Sciences Trust II (BMEZ). In the case of those funds, there was actually some material difference that resulted in a relatively more meaningful reduction in correlation. For those two funds, we provided examples of what other funds had provided higher correlations.
Going back to BST and BSTZ, we can take a look at the YTD performance. Both funds have experienced some sharp losses on a total NAV return basis. The total share price returns have diverged quite materially. That's what presents a better opportunity for swap candidates.
In the case of BSTZ, the fund's share price has fallen more rapidly than the fund's NAV return. Of course, that opens up a discount for the fund. It is precisely what we are looking for when wanting to invest in closed-end funds.
On the other hand, BST's share price has performed better than its NAV. That has resulted in the opposite of what we want to see. At least the opposite of what we want to see if we are looking to invest in a fund. For shareholders already in the fund, it can be beneficial.
One last comparison I wanted to include was the relative performance of BST and BSTZ to some similar ETFs. For this, we will use Invesco QQQ (QQQ) - a proxy for the Nasdaq 100. This holds a lot of other non-tech-type holdings but can provide some context. We will also use ARK Innovation (ARKK) because ARKK holds a lot of smaller and speculative companies that are associated with BSTZ's portfolio.
In this case, we can see that ARKK has done the worst by a long shot. We can also see that QQQ has held up relatively better, thanks presumably to the holdings outside of tech.
Even if we go further back, the plunge in ARKK has now been so deep and rapid that BST and BSTZ are still better performing. Some of the defensiveness here can be due to the options writing that the funds undertake. However, the largest factor is going to be the positions of the funds overall.
BST has a longer history than BSTZ. Going back to a launch in 2014, with BSTZ having launched in 2019. That gives us significantly less of a track record when looking at the discount history for BSTZ.
What history we do have would suggest that at this time, BSTZ is quite a compelling valuation. BST is right near its historical average. That would suggest that it isn't necessarily a strong buy or a strong sell, more of a hold or neutral rating. That being said, we can see that shortly after the fund launch until around 2018, the fund's discount was rather persistent.
Historically, tech isn't an area that pays out really high dividends to investors. In the case of BST and BSTZ, the results of the CEF structure allow for monthly distributions from these funds. The distributions can come in the form of income, gains and return of capital. For these funds, neither has a positive net investment income figure, meaning they will rely entirely on capital gains or return of capital.
Both BST and BSTZ had their entire distributions classified as long-term capital gains in 2021. Official tax data for this year won't be known until year-end. Due to the trajectory of the market and challenges, we wouldn't be too surprised if we see return of capital in their distributions.
While capital gains can become harder to come by in this tech sell-off that is continuing to grind these funds lower and lower, all hope isn't lost. They have built-in capital gains that they can continue to utilize. At the end of 2021, BSTZ had over $1.5 billion in unrealized gains, and BST had nearly $886 million. These figures would have come down at this point, with losses in the underlying portfolios continuing.
Despite almost everything tech-related being sold off, they can still produce gains by buying low and selling high. Often, this isn't the case with CEFs, as that would require very active management.
Additionally, this is where the options strategy can come in handy too. Traditionally, the covered call writing on individual names in the portfolio had resulted in losses for the funds. We can see this for both funds in 2021. It wasn't massive losses relative to the size of these funds, but losses nonetheless.
This can happen when they buy a position back to close it before being called away. However, with the losses accumulating this year, they shouldn't be buying positions back to get out of the contracts. This should provide realized capital gains for the funds this year. That can offset some of the declines experienced in the funds.
Since capital gains will be harder to come by, I believe both funds are at risk of distribution cuts if things don't improve. BSTZ is more susceptible as the fund's NAV distribution rate pushes higher at 9.59%. For BST, they are at an 8.38% distribution rate on the NAV. Either way, these funds will remain an attractive place to look for "income" from tech holdings where tech would otherwise normally pay relatively smaller yields.
As mentioned previously, BST was traditionally holding onto larger cap names. They still are but have been shifting more towards small-cap names. Here's the latest that shows they have once again shifted a bit more to small-cap names. The average market cap of their holdings is $581,353.9 million.
Perhaps, some of this even being the result of mid-cap or large-cap names falling all the way to small-cap status. It isn't too wild to think, considering some of the tech names have seen drops of 70% or more in their share price.
The small-cap exposure had really picked up over the last year. Looking at the weighting of last June 2021, small-cap was only around 17% of the portfolio. It was also an increase from even before that when no project names showed up in 2020. This shift will either turn out to be genius in the long run or poorly timed. The verdict is still out.
Looking at the top ten, they still hang onto names such as Apple (AAPL), Microsoft (MSFT) and Alphabet (GOOG). However, we have a couple of the "project" names in the top ten. Those are associated with private investments that are found more commonly in BSTZ.
When looking at BSTZ, we still see a higher relative weighting to small-caps, and mid-caps also make up a meaningful allocation. Large-cap weightings were nearly 52% of the fund previously. A sharp drop in tech presumably provided some of the declining weightings here too. The average market cap weighting for BSTZ comes to $53,454.1 million.
That's a substantial difference from BST. It goes back to AAPL, MSFT and GOOG, though, where those names disproportionately raise the average market cap of the underlying holdings. They are between $1.5 and $2.5 trillion market cap companies.
In terms of the top ten of BSTZ, we see some new project names. Again, these are their private investment codenames. Project Bond, Project Salinger and Project Gaugamela are new appearances on this list from our previous BST/BSTZ piece. We now have seven project names on the list from the previous four at the beginning of the year. This was six just earlier this year, too, in my most exact update on BSTZ. Project Gaugamela is the latest addition. They do not have their second-quarter commentary available at this time.
Earlier in July, Klarna (Project Kafka) had its valuation drop again when they raised a new round of cash. That would suggest that we will see BSTZ reprice that position lower when they update.
If you want a clear, concise portfolio where you know what you are holding. BSTZ probably isn't it. I've done my best to try and nail down what some of these project names are referring to. Klarna is an easy one, but they seem to make it difficult.
One way I thought it would be easy to compare is when they list the holdings on their site and then refer to their year-end annual report.
Here we see a snapshot of the December 2021 holdings for BSTZ.
Go to the year-end report, which is also the same December 31st, 2021, reporting date, and this is what we get.
We see here that the top ten holdings on their website aren't exactly making sense because they are top ten "exposure," not specific holdings.
See Project Debussy? That's Databricks. How can that be? This is because they hold three different positions in Databricks that combined are $106,513,808 in value at the end of 2021. Against the total net assets at that time, it works out to around 3.5% or close enough to the 3.48% they have listed.
Yet, the annual report shows that Databricks is listed as the eighth largest weighting. That's because they have one position - the Series F round at $62,212,977 - working out to 2.04% or the 2% they list.
BST and BSTZ continue getting slammed into the tech sell-off that doesn't seem to be letting up. With Snap (SNAP) reporting poor results and crashing 38.6% at the time of this writing, it doesn't seem that tech is ready to rebound yet.
These will be longer-term positions in my portfolio. If I were putting capital to work today, I'd lean more towards BSTZ with the larger discount. However, with the heavier private investments, the discount here could be more appropriate. Private investments can be restricted, and the valuation unknown until they go to sell. One would have to be okay with this higher risk proposition before investing in BSTZ.
BST continues its trend towards investing in smaller companies, leaning into BSTZ's space. This will either result in them looking like geniuses in the long run, or I'll just have two poor-performing funds in the end.
Customers of Databricks can now use the collaborative workspace for analytics and data science through Databricks Partner Connect.
FREMONT, CA: "The ability to easily combine Python, SQL, and no-code cells in an elegant UI on top of the Databricks Lakehouse means a much broader set of analytically curious people can work together," states Barry McCardel, CEO and co-founder of Hex. Hex, a collaborative data science and analytics platform provider has extended its availability to Databricks customers through Databricks Partner Connect. With Hex on Databricks, users can connect to the Databricks Lakehouse Platform, analyze their data in collaborative SQL and Python-powered notebooks, and publish their work as publicly accessible interactive data applications.
"Feedback loops are shorter, data products are better, and organizations build knowledge faster. We encourage all Databricks users to try Hex today from Partner Connect," adds McCardel.
Hex was developed to enable users of various skill levels to make data-driven decisions. The analytics workspace is frequently deployed as the front-end of the current data stack due to its simple yet elegant design and superior integration with other critical components such as Databricks, which offers unrivaled data processing and storage.
With native SQL support, Hex users may construct robust analyses and data applications using only Databricks queries. Alternatively, they can switch to Python and advantage of its abundant package support. Hex's flexible notebook-based workspace allows users to upload files, conduct REST API calls, and duplicate other data notebook tasks while working with unstructured or non-SQL-friendly Databricks tasks.
Customers of Databricks can click the Hex tile from Databricks Partner Connect to begin a Hex trial within Databricks, immediately generating objects or allowing access to their preferred database to fuel a Hex project.
"Many Databricks customers rely on the lakehouse to power strategic analytics initiatives such as Customer 360 programs or fraud prevention efforts," states Roger Murff, VP of ISV Partners at Databricks. "Hex makes it easier for domain experts and skilled data scientists to reach insights together and build apps that directly impact and drive value for their business. I look forward to seeing what customers accomplish when they use Hex on Databricks."
In this article, we will discuss the 14 most valuable venture-backed companies in the world. You can skip our detailed analysis of these companies, and go directly to 5 Most Valuable Venture-Backed Companies in the World.
Venture-backed companies are usually startups that have private equity-based financing from investors. These investors are known as venture capitalists and can include public and private corporations, and individuals. The venture capitalists can take up to 25% to 50% of a startup’s ownership. Venture capitalists are not hobbyists but professionals who provide capital assistance, guidance and support to startups.
Venture capitalists are experts at forecasting growth and evaluating potential of companies. But not all their investments are fruitful. As a rule of thumb, out of 10 startups, only one or two return profits on investments. The other three to four fail and the rest just manage to reach break-even levels. One of the examples of a successful return on investment is Sequoia Capital’s $60 million investment in WhatsApp which got converted into $3 billion when Facebook Inc. (NASDAQ: FB) acquired WhatsApp for $22 billion. Some other notable successful companies that were backed by VCs include Airbnb Inc (NASDAQ:ABNB), DoorDash Inc (NYSE:DASH) and Robinhood Markets Inc (NASDAQ:HOOD).
In the year 2020, when businesses saw a downward trend due to COVID-19, venture funding saw an annual increase of 4% with total funding amounting to $300 billion. This growth is attributed to an increase in the use of technology-based online solutions for work, health, education, finance, and shopping.
Valuation: $16 billion
One97 Communications is based in India and operates in the fintech industry. One97 Communications offers financial services through its platform Paytm. Paytm allows e-commerce sellers to use semi-closed wallets for accepting payments. Some of the major investors in One97 Communications are Intel Capital, Sapphire Ventures, and Alibaba Group. Founded in 2000, One97 Communications has raised $4.73 billion. One97 Communications earned a revenue of $380 million in 2020.
Valuation: $18 billion
BYJU’S is an Indian startup with operations in the Ed-tech industry. BYJU’S offers a K-12 learning application that allows students to learn for classes 4 to 12 and prepare for competitive exams like JEE, NEET, CAT, GRE, GMAT, and IAS. Since its inception in 2015, BYJU’S has raised $3.62 billion. The investor count of BYJU’S is 41, with major investments from Tencent Holdings Limited (HKSE: 0700. H.K.), Lightspeed India Partners, and Sequoia Capital India. BYJU’S earned a revenue of $800 million in 2021.
Valuation: $27 billion
Fanatics is based in the U.S. and works in the e-commerce and direct-to-consumer industry. Fanatics offers merchandising, marketing, fulfillment, and e-commerce services to professional sports leagues, collegiate teams, conferences, and other major sports events. Fanatics works with its own collection of merchandise on its e-commerce store and customized ordering service on its B2B website. The major venture capitalists investing in Fanatics include SoftBank Group Corp. (OTC: SFTBY), Andreessen Horowitz, and Temasek Holdings. Fanatics earned a revenue of $2.6 billion in 2020.
Valuation: $25 billion
FTX, a Hong-Kong based company, operates in the Fintech industry. FTX is a cryptocurrency exchange providing traders a platform to trade online. FTX also offers derivatives, options, and tokenized stocks. FTX has an investor count of 31 with major investors like Sequoia Capital, Thoma Bravo, and SoftBank Group Corp. (OTC: SFTBY).
Valuation: $25 billion
Chime is based in San Francisco and operates in the fintech industry. Chime offers a mobile banking app that is aimed to reduce banking fees and automate savings. As of September 2021, Chime has raised more than $2.44 billion since its inception in 2013. Chime has an investor count of 24 with major investors like Forerunner Ventures, Crosslink Capital, and Homebrew.
Valuation: $32 billion
Epic Games is based in North Carolina and works in the Software/Gaming industry by developing video games. Some of the well-known games by Epic Games include the Gears of War series. The Unreal Engine technology of Epic Games is also very popular. Since its foundation in 1991, Epic Games has raised $4.375 billion from major capitalists like Tencent Holdings Limited (HKSE: 0700. H.K.), KKR, and Smash Ventures.
However, gaming companies and tech stocks like Airbnb Inc (NASDAQ:ABNB), DoorDash Inc (NYSE:DASH) and Robinhood Markets Inc (NASDAQ:HOOD) are feeling the brunt of the slowing economic activity amid recession fears.
Nubank operates in the fintech industry and is based in Sao Paulo, Brazil. Nubank offers a no-fee credit card to the Brazilian market. The card is managed through Nubank mobile application. The company has raised over $2.547 billion in investments since its incorporation in 2014. The total investor count of Nubank is 28, with major VCs like Sequoia Capital, Redpoint E.Ventures Management, LLC., and Kaszek Ventures.
Valuation: $33 billion
Revolut is based in London and operates in Mobile Software & Payments industry. Founded in 2015, the company has raised over $1.716 billion in venture investments. Revolut aims to remove the hidden banking costs by providing money exchange services at interbank rates, multi-currency cards, and money transfers using the social network. Revolut has over 30 investors, including major venture capitalists like Index Ventures, DST Global, and Ribbit Capital.
Venture capital activity has slowed down in 2022 amid recession fears and broader rotation away from growth stocks like Airbnb Inc (NASDAQ:ABNB), DoorDash Inc (NYSE:DASH) and Robinhood Markets Inc (NASDAQ:HOOD).
Valuation: $22 billion
Data Bricks is based in San Francisco and provides Data and Document Management services. Since its inception in 2013, Databricks has raised $3.497 billion, as of September 2021. Databricks offers a data platform for simplification of data integration and allows data analytics services. Databrick has over 29 investors with major names, including Andreessen Horowitz, New Enterprise Associates, and Battery Ventures.
Click to continue reading and see 5 Most Valuable Venture-Backed Companies in the World.
Disclosure. None. 15 Most Valuable Venture-Backed Companies in the World is originally published on Insider Monkey.
Leading consulting partners like Avanade, Deloitte, Lovelytics and Tredence build industry-specific data and AI solutions to accelerate customer value on the Databricks Lakehouse Platform
SAN FRANCISCO, March 17, 2022 /PRNewswire/ -- Databricks, the Data and AI company and pioneer of the data lakehouse paradigm, is increasing its investment in its growing partner ecosystem as more customers adopt the lakehouse. Today, Databricks is announcing the debut of Brickbuilder Solutions: data and AI solutions expertly designed by leading consulting partners to address industry-specific business requirements on the Databricks Lakehouse Platform.
As part of the launch, Databricks has partnered with industry-leaders like Avanade, Deloitte, Lovelytics and Tredence to jointly develop and implement a suite of solutions that help customers solve their analytics challenges, adopt more agile processes, and break into new revenue streams. From demand forecasting and revenue growth management solutions to value-at-risk data models and AI-powered recommendation engines, these Brickbuilder Solutions combine the power of the Databricks Lakehouse Platform with the proven experience of partners to accelerate data and AI use cases for organizations across every industry.
Looking ahead, Databricks will continue to build industry-specific lakehouses. The added investment in industry innovation is a key focus area for Kori O'Brien, SVP of Global Consulting and SI Partners who joined the company last year from Salesforce, where she spent a decade building and leading a robust global partner organization and most recently served as the SVP of Alliances and Partner Sales.
"Databricks is pioneering the lakehouse category and has an incredible opportunity to build and shape an ecosystem for partners to thrive," said Kori O'Brien, SVP of Global Consulting and SI Partners at Databricks. "We're excited to double-down on our partner initiatives this year, as they are a critical force in helping customers derive value from data and AI. These efforts include expanding our global partner team and investing in infrastructure and training programs that will deliver a best-in-class experience for our rapidly expanding partner organization."
Andy Kofoid, President of Global Field Operations at Databricks, added, "Partners play an integral role in expanding and scaling Databricks' go-to-market function and bringing the power of the lakehouse to more customers around the world."
Brickbuilder Solutions is one of several ways in which Databricks is enabling and accelerating an ecosystem around its Lakehouse Platform recently, including:
Extending the power of Databricks' Lakehouse Platform and delivering deeper functionality for customers with Brickbuilder Solutions
"Along with Databricks, we have combined the significant investments and strengths of both companies to meet and support client demand through industry solutions. We have jointly released customer personalization accelerators and low risk enterprise Hadoop migration to accelerate business results for Financial Services clients. Most recently, we worked closely with Databricks to develop Risk Management for Financial Services, a suite of lakehouse solutions covering regulatory, financial and business risk solutions," said Alan Grogan, Executive Lead, Data Platform Modernization at Avanade.
"Our clients are looking to simplify data and analytics architectures as they unlock business value through the power of cloud and AI technologies," said Maulik Shah, Databricks Alliance Leader, and Managing Director, Deloitte Consulting LLP. "Our clients are taking an AI-first approach to migrating and modernizing legacy data platforms to the cloud â€“ in addition to the traditional BI and SQL use-cases, they are architecting for forward-looking use-cases, such as real-time, data sharing and interop, AI/ML, and multi-cloud, to achieve long-term and sustainable business value."
Francisco Barroso, Analytics & Cognitive Offering Leader, and Principal, Deloitte Consulting LLP, added, "Deloitte's deep industry and client experiences across the commercial and public sectors, as well as our experience building integrated, secure, and multi-cloud ready solutions on top of the Databricks Lakehouse Platform, has been a game changer for our clients. Now, they are able to focus on critical shifts in their business, assess data- and AI-driven insights, and see significant improvement in both top- and bottom-line performance â€“ all at a faster pace, with higher quality, and with lower costs. We are excited about the continued collaboration with Databricks and continue to invest heavily and relentlessly in innovations for our joint clients."
As organizations around the world embrace the data lakehouse paradigm, they can confidently adopt powerful data solutions and services that are customized for the lakehouse and backed by world-class consulting partners who have deep technical expertise and implementation experience with the modern data stack and Databricks' platform.
Visit the Databricks blog to learn more about Brickbuilder Solutions available today. Databricks partners who are interested in learning more about Brickbuilder Solutions are encouraged to attend Databricks Partner Kickoff on March 28th. To register for the event, visit https://databricks.swoogo.com/PKO. You must be an official Databricks partner to register.
Databricks is the data and AI company. More than 7,000 organizations worldwide â€” including Comcast, CondÃ© Nast, H&M, and over 40% of the Fortune 500 â€” rely on the Databricks Lakehouse Platform to unify their data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe. Founded by the original creators of Apache Sparkâ„¢, Delta Lake and MLflow, Databricks is on a mission to help data teams solve the world's toughest problems. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.
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Hightouch Expands Partnership with Databricks to Help Joint Customers Sync Data Insights with Business Tools
SAN FRANCISCO, July 19, 2022 /PRNewswire/ -- Hightouch, the world's leading Data Activation platform, today announced its native integration with Databricks, expanding the partnership between the two companies and providing joint customers with a seamless way to activate data in their data lakehouse. With Hightouch now available through Partner Connect, Databricks users can easily send data to strategic destinations, automate the provisioning of a Hightouch workspace, and sync the business rules defined in the lakehouse—all directly from Databricks Partner Connect.
Developers can now quickly activate the unique data models, event data, and product usage data that lives in the lakehouse to power mission-critical use cases that help business teams drive revenue, reduce churn, optimize marketing campaigns, and more. Hightouch automatically syncs relevant customer data into over 100 SaaS destinations so engineers can spend less time manually exporting static files or building custom pipelines, and more time driving strategic business value.
"Hightouch makes it trivial to pull data out of Databricks directly into the business systems that are home to business users and their workflows," explains Dr. Ernie Prabhakar, Business Data Lead at Nauto, an AI-enabled driver & fleet safety company already taking advantage of the Hightouch integration with Databricks. "With Hightouch, employees can leverage a single source of truth about our organization's data to make better mission-critical decisions when it matters most."
Databricks customers can now empower their various teams to run business-critical applications from within their data lakehouse. "With Hightouch, customers can tackle more business workflows while maintaining a single source of truth for their data," explains Steve Sobel, Global Industry Leader for Media & Entertainment at Databricks. "We are especially excited about Hightouch enhancing the Customer Data Platform capabilities for the lakehouse."
The powerful integration between Hightouch and Databricks turns the lakehouse into a Customer Data Platform (CDP) and opens new opportunities for business teams including:
"With Partner Connect, Hightouch continues to advance the data activation category," explains Tejas Manohar, CEO and co-Founder at Hightouch. "Our extended partnership with Databricks makes it seamless for organizations to create activation workloads that immediately impact revenue, from improving ad spend to increasing marketing conversions to reducing customer churn."
Hightouch is now available in Databricks Partner Connect, so get started today.
Hightouch is the world's leading Data Activation platform, syncing data from warehouses directly into SaaS tools. In November, Hightouch announced $40M in Series B financing at a $450 million valuation led by ICONIQ Capital. Hightouch's hundreds of customers range from fast-growing startups like Plaid, Betterment, Calendly, and Lucidchart to large enterprises like AXS, Nando's, and Autotrader. For more information, visit www.hightouch.com.
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SAN FRANCISCO, June 28, 2022 /PRNewswire/ -- Databricks, the data and AI company and pioneer of the data lakehouse paradigm, today unveiled the evolution of the Databricks Lakehouse Platform to a sold-out crowd at the annual Data + AI Summit in San Francisco. New capabilities revealed include best-in-class data warehousing performance and functionality, expanded data governance, new data sharing innovations to include an analytics marketplace and data clean rooms for secure data collaboration, automatic cost optimization for ETL operations, and machine learning (ML) lifecycle improvements.
"Our customers want to be able to do business intelligence, AI, and machine learning on one platform, where their data already resides. This requires best-in-class data warehousing capabilities that can run directly on their data lake. Benchmarking ourselves against the highest standards, we have proven time and again that the Databricks Lakehouse Platform gives data teams the best of both worlds on a simple, open, and multi-cloud platform," said Ali Ghodsi, Co-founder and CEO of Databricks. "Today's announcements are a significant step forward in advancing our Lakehouse vision, as we are making it faster and easier than ever to maximize the value of data, both within and across companies."
The Best Data Warehouse is the Lakehouse
Organizations like Amgen, AT&T, Northwestern Mutual and Walgreens, are making the move to the lakehouse because of its ability to deliver analytics on both structured and unstructured data. Today, Databricks unveiled new data warehousing capabilities in its platform to further enhance analytics workloads:
Data Governance Highlighted as a Top Priority with Advanced Capability for Unity Catalog
Unity Catalog, generally available on AWS and Azure in the coming weeks, offers a centralized governance solution for all data and AI assets, with built-in search and discovery, automated lineage for all workloads, with performance and scalability for a lakehouse on any cloud. Also, Databricks introduced data lineage for Unity Catalog earlier this month, significantly expanding data governance capabilities on the lakehouse and giving businesses a complete view of the entire data lifecycle. With data lineage, customers gain visibility into where data in their lakehouse came from, who created it and when, how it has been modified over time, how it's being used across data warehousing and data science workloads, and much more.
Enhanced Data Sharing Enabled By Databricks Marketplace and Cleanrooms
As the first marketplace for all data and AI, available in the coming months, Databricks Marketplace provides an open marketplace to package and distribute data and analytics assets. Going beyond marketplaces that simply offer datasets, Databricks Marketplace enables data providers to securely package and monetize a host of assets such as data tables, files, machine learning models, notebooks and analytics dashboards. Data consumers can easily discover new data and AI assets, jumpstart their analysis and gain insights and value from data faster. For example, instead of acquiring access to a dataset and investing their own time to develop and maintain dashboards to report on it, they can choose to simply subscribe to pre-existing dashboards that already provide the necessary analytics. Databricks Marketplace is powered by Delta Sharing, allowing data providers to share their data without having to move or replicate the data from their cloud storage. This allows providers to deliver data to other clouds, tools, and platforms from a single source.
Databricks is also helping customers share and collaborate with data across organizational boundaries. Cleanrooms, available in the coming months, will provide a way to share and join data across organizations with a secure, hosted environment and no data replication required. In the context of media and advertising, for example, two companies may want to understand audience overlap and campaign reach. Existing clean room solutions have limitations, as they are commonly restricted to SQL tools and run the risk of data duplication across multiple platforms. With Cleanrooms, organizations can easily collaborate with customers and partners on any cloud and provide them the flexibility to run complex computations and workloads using both SQL and data science-based tools - including Python, R, and Scala - with consistent data privacy controls.
MLflow 2.0 Streamlines and Accelerates Production Machine Learning at Scale
Databricks continues to lead the way in MLOps innovation with the introduction of MLflow 2.0. Getting a machine learning pipeline into production requires setting up infrastructure, not just writing code. This can be difficult for new users and tedious for everyone at scale. MLflow Pipelines, made possible by MLflow 2.0, now handles the operational details for users. Instead of setting up orchestration of notebooks, users can simply define the elements of the pipeline in a configuration file and MLflow Pipelines manages execution automatically. Looking beyond MLflow, Databricks also added Serverless Model Endpoints to directly support production model hosting, as well as built-in Model Monitoring dashboards to help teams analyze the real-world model performance.
Delta Live Tables Includes Industry First Performance Optimizer for Data Engineering Pipelines
Delta Live Tables (DLT) is the first ETL framework to use a simple, declarative approach to building reliable data pipelines. Since its launch earlier this year, Databricks continues to expand DLT with new capabilities including the introduction of a new performance optimization layer designed to speed up execution and reduce costs of ETL. Additionally, new Enhanced Autoscaling is purpose-built to intelligently scale resources with the fluctuations of streaming workloads, and Change Data Capture (CDC) for Slowly Changing Dimensions - Type 2, easily tracks every change in source data for both compliance and machine learning experimentation purposes.
To learn more about the Databricks Lakehouse Platform visit: https://databricks.com/product/data-lakehouse. Tune in virtually for more Data + AI Summit keynotes by registering here for the free, immersive online experience.
Databricks is the data and AI company. More than 7,000 organizations worldwide â€” including Comcast, CondÃ© Nast, H&M, and over 40% of the Fortune 500 â€” rely on the Databricks Lakehouse Platform to unify their data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe. Founded by the original creators of Delta Lake, Apache Sparkâ„¢, and MLflow, Databricks is on a mission to help data teams solve the world's toughest problems. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.
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This information is provided to outline Databricks' general product direction and is for informational purposes only. Customers who purchase Databricks services should make their purchase decisions relying solely upon services, features, and functions that are currently available. Unreleased features or functionality described in forward-looking statements are subject to change at Databricks discretion and may not be delivered as planned or at all.
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Silicon Valley prides itself on wunderkind tech founders who generate huge sums of money for their Bay Area companies and, sometimes, change industries virtually overnight.
But while the Silicon Valley reaps much of the praise for the technology and ideas coming out of the region, it's a sector relying on immigrants to lead it. More than half of startups valued at US$1bil (RM4.46bil) or more were founded by immigrants to the United States, according to a report from the nonprofit National Foundation for American Policy.
The report found that of the 582 "unicorn" startups valued at $1bil or more in the US, 319 of them, or 55% had at least one immigrant founder. That number rose to two-thirds when counting companies that were founded or co-founded by immigrants, or the children of immigrants.
Of those 319 companies, 153, or 48%, were founded in the Bay Area, including top industry players such as Stripe, Brex, Instacart, Databricks and dozens of others.
Stripe, an online payment processing company, was founded by John and Patrick Collison from Ireland, while Brex, a financial service company, was started by Henrique Dubugras and Pedro Franceschi from Brazil. Canadian immigrant Apoorva Mehta started shopping delivery service Instacart, while software company Databricks' founding team is composed of Iranian, Romanian and Chinese nationals.
The findings point to the ongoing centrality of immigration as a driver of one of the United States' most valuable industries, even though foreign nationals face significant barriers to starting a business in the US.
"The findings in the study are noteworthy given there is generally no reliable way under US immigration law for foreign nationals to start a business and remain in the country after founding the company," the report said.
Immigrant company founders almost always come to the US as refugees, through family reunification visa routes, or on employer sponsored visas like H-1Bs, which are limited in duration and frequently don't allow someone to stay permanently, the report said.
"A startup visa to allow foreign nationals who found companies and create jobs would be a critical addition to the US immigration system since it can be difficult for foreign-born entrepreneurs to stay and grow their business," the report by NFAP Executive Director Stuart Anderson said.
The administration of President Barack Obama created a program to allow international entrepreneurs to come to the US temporarily to work on business ventures, but it didn't provide a route to a more permanent visa to stay in the country.
The administration of President Donald Trump shuttered the program before it was restarted last year under the Biden administration. It's unclear how many people have used the program, especially as the pandemic slowed international travel and immigration to a crawl over the past two years.
The Trump administration also created significant cutbacks and processing delays for programs that allowed foreign-born people to work and study in the US, premised on the idea they were taking jobs for lower pay from US-born workers.
The program was designed to fill highly skilled jobs when a US citizen can't be found to fill the job, although it has been used by some large staffing companies to undercut wages in some cases.
Another long-running issue is the huge backlog for certain nationalities to transition to employment-based permanent residency, or green cards, even after being in the US for several years on a high-skilled temporary H-1B visa.
The report found that 66 founders of US startups worth US$1bil were from India, while 54 were from Israel, 27 were from the United Kingdom, 22 from Canada and 21 from China.
But employment-based green cards are subject to country quotas, and the backlog for India could top two million people by the end of the decade, according to a Congressional Research Service estimation referenced in the report.
While US universities have long been high-tech proving grounds for talented foreign students, it is also difficult for them to stay in the country after they graduate. H-1B visas and extensions for those who get jobs are an option, but more than 80% of people who apply for the specialty visas are rejected via lottery.
That makes it difficult for young tech talent to stay in the country to work for, or eventually start, the next company worth US$1bil or more. – San Francisco Chronicle/Tribune News Service
At its Data + AI Summit, Databricks today made the requisite number of announcements one would expect from a company's flagship developer event. Among those are the launch of Delta Lake 2.0, the next version of its platform for building data lakehouses, MLflow 2.0, the next generation of its platform for managing the machine learning pipeline, which now includes MLflow Pipelines with templates for bootstrapping model development and a couple of announcements around the Apache Spark data analytics engine, which forms part of the core of the Databricks platform.
With Spark Connect, Databricks today announced a new client and server interface for Spark that is based on the DataFrame API. In Spark, a DataFrame is a distributed collection of data that is organized into columns and made available through an API in languages like Scala, Java, Python or R. With Spark Connect, Databricks takes this concept but then decouples the client and server, which the company says will lead to better stability and enables remote connectivity as a built-in feature.
What's maybe more exciting, though, is something Databricks calls Project Lightspeed, which the company describes as the next generation of the Spark streaming engine. Databricks argues that as more applications now require streaming data, the requirements for what streaming engines can provide have also changed.
"Spark Structured Streaming has been widely adopted since the early days of streaming because of its ease of use, performance, large ecosystem and developer communities," the company explains in today's announcement. "With that in mind, Databricks will collaborate with the community and encourage participation in Project Lightspeed to Improve performance, ecosystem support for connectors, enhance functionality for processing data with new operators and APIs, and simplify deployment, operations, monitoring and troubleshooting."
A Databricks spokesperson told me that the project will be led by Karthik Ramasamy, the company's head of streaming, with a focus on delivering higher throughput, lower latency and lower cost, as well as an expanded ecosystem of connectors and additional data processing functionality.
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