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Jitsu nabs $2M seed to build open-source data integration platform
Jitsu, a graduate of the Y Combinator Summer 2020 cohort, is developing an open-source data integration platform that helps developers send data to a data warehouse. Today, the startup announced a $2 million seed investment.
Costanoa Ventures led the round with participation from Y Combintaor, The House Fund and SignalFire.
In addition to the open-source version of the software, the company has developed a hosted version that companies can pay to use, which shares the same name as the company. Peter Wysinski, Jitsu’s co-founder and CEO, says a good way to think about his company is an open-source Segment, the customer data integration company that was recently sold to Twilio for $3.2 billion.
But, he says, it goes beyond what Segment provides by allowing you to move all kinds of data, whether customer data, connected device data or other types. “If you look at the space in general, companies want more granularity. So let’s say for example, a couple years ago you wanted to sync just your transactions from QuickBooks to your data warehouse, now you want to capture every single sale at the point of sale. What Jitsu lets you do is capture essentially all of those events, all of those streams, and send them to your data warehouse,” Wysinski explained.
Among the data warehouses it currently supports are Amazon Redshift, Google BigQuery, PostGres and Snowflake.
The founders built the open-source project called EventNative to help solve problems they themselves were having moving data around at their previous jobs. After putting the open-source version on GitHub a few months ago, they quickly attained 1,000 stars, proving that they had delivered something that solved a common problem for data teams. They then built the hosted version, Jitsu, which went live a couple of weeks ago.
For now, the company is just the two co-founders, Wysinski and CTO Vladimir Klimontovich and couple of contract engineers, but they intend to do some preliminary hiring over the next year to grow the company, most likely adding engineers. As they begin to build out the startup, Wysinski says that being open source will help drive diversity and inclusion in their hiring.
“The goal is essentially to go after that open-source community and hire people from anywhere because engineers aren’t just […] one color or one race, they’re everywhere, and being open source, and especially being in a remote world, makes it so, so much simpler [to build a diverse workforce], and a lot of companies I feel are going down that road,” he said.
He says along that line, the plan is to be a fully remote company, even after the pandemic ends, as they hire from anywhere. The goal is to have quarterly offsite meetings to check in with employees, but do the majority of the work remotely.
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Databricks launches SQL Analytics
AI and data analytics company Databricks today announced the launch of SQL Analytics, a new service that makes it easier for data analysts to run their standard SQL queries directly on data lakes. And with that, enterprises can now easily connect their business intelligence tools like Tableau and Microsoft’s Power BI to these data repositories as well.
SQL Analytics will be available in public preview on November 18.
In many ways, SQL Analytics is the product Databricks has long been looking to build and that brings its concept of a “lake house” to life. It combines the performance of a data warehouse, where you store data after it has already been transformed and cleaned, with a data lake, where you store all of your data in its raw form. The data in the data lake, a concept that Databricks’ co-founder and CEO Ali Ghodsi has long championed, is typically only transformed when it gets used. That makes data lakes cheaper, but also a bit harder to handle for users.
Image Credits: Databricks
“We’ve been saying Unified Data Analytics, which means unify the data with the analytics. So data processing and analytics, those two should be merged. But no one picked that up,” Ghodsi told me. But “lake house” caught on as a term.
“Databricks has always offered data science, machine learning. We’ve talked about that for years. And with Spark, we provide the data processing capability. You can do [extract, transform, load]. That has always been possible. SQL Analytics enables you to now do the data warehousing workloads directly, and concretely, the business intelligence and reporting workloads, directly on the data lake.”
The general idea here is that with just one copy of the data, you can enable both traditional data analyst use cases (think BI) and the data science workloads (think AI) Databricks was already known for. Ideally, that makes both use cases cheaper and simpler.
The service sits on top of an optimized version of Databricks’ open-source Delta Lake storage layer to enable the service to quickly complete queries. In addition, Delta Lake also provides auto-scaling endpoints to keep the query latency consistent, even under high loads.
While data analysts can query these data sets directly, using standard SQL, the company also built a set of connectors to BI tools. Its BI partners include Tableau, Qlik, Looker and ThoughtSpot, as well as ingest partners like Fivetran, Fishtown Analytics, Talend and Matillion.
Image Credits: Databricks
“Now more than ever, organizations need a data strategy that enables speed and agility to be adaptable,” said Francois Ajenstat, chief product officer at Tableau. “As organizations are rapidly moving their data to the cloud, we’re seeing growing interest in doing analytics on the data lake. The introduction of SQL Analytics delivers an entirely new experience for customers to tap into insights from massive volumes of data with the performance, reliability and scale they need.”
In a demo, Ghodsi showed me what the new SQL Analytics workspace looks like. It’s essentially a stripped-down version of the standard code-heavy experience with which Databricks users are familiar. Unsurprisingly, SQL Analytics provides a more graphical experience that focuses more on visualizations and not Python code.
While there are already some data analysts on the Databricks platform, this obviously opens up a large new market for the company — something that would surely bolster its plans for an IPO next year.
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Microsoft’s Project Natick underwater data center experiment confirms viability of seafloor data storage
Microsoft has concluded a years-long experiment involving use of a shipping container-sized underwater data center, placed on the sea floor off the cost of Scotland’s Orkney Islands. The company pulled its “Project Natick” underwater data warehouse up out of the water earlier this year (at the beginning of the summer) and spent the last few months studying the data center, and the air it contained, to determine the model’s viability.
The results not only showed that using these offshore submerged data centers seems to work well in terms of performance, but also revealed that the servers contained within the data center proved to be up to eight times more reliable than their dry-land counterparts. Researchers will be looking into exactly what was responsible for this greater reliability rate in the hopes of also translating those advantages to land-based server farms for increased performance and efficiency across the board.
Other advantages included being able to operate with greater power efficiency, especially in regions where the grid on land is not considered reliable enough for sustained operation. That’s due in part to the decreased need for artificial cooling for the servers located within the data farm because of the conditions at the sea floor. The Orkney Island area is covered by a 100% renewable grid supplied by both wind and solar, and while variances in the availability of both power sources would’ve proven a challenge for the infrastructure power requirements of a traditional, overland data center in the same region, the grid was more than sufficient for the same size operation underwater.
Microsoft’s Natick experiment was meant to show that portable, flexible data center deployments in coastal areas around the world could prove a modular way to scale up data center needs while keeping energy and operation costs low, all while providing smaller data centers closer to where customers need them, instead of routing everything to centralized hubs. So far, the project seems to have done spectacularly well at showing that. Next, the company will look into seeing how it can scale up the size and performance of these data centers by linking more than one together to combine their capabilities.
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RudderStack raises $5M seed round for its open-source Segment competitor
RudderStack, a startup that offers an open-source alternative to customer data management platforms like Segment, today announced that it has raised a $5 million seed round led by S28 Capital. Salil Deshpande of Uncorrelated Ventures and Mesosphere/D2iQ co-founder Florian Leibert (through 468 Capital) also participated in this round.
In addition, the company also today announced that it has acquired Blendo, an integration platform that helps businesses transform and move data from their data sources to databases.
Like its larger competitors, RudderStack helps businesses consolidate all of their customer data, which is now typically generated and managed in multiple places — and then extract value from this more holistic view. The company was founded by Soumyadeb Mitra, who has a Ph.D. in database systems and worked on similar problems previously when he was at 8×8 after his previous startup, MairinaIQ, was acquired by that company.
Mitra argues that RudderStack is different from its competitors thanks to its focus on developers, its privacy and security options and its focus on being a data warehouse first, without creating yet another data silo.
“Our competitors provide tools for analytics, audience segmentation, etc. on top of the data they keep,” he said. “That works well if you are a small startup, but larger enterprises have a ton of other data sources — at 8×8 we had our own internal billing system, for example — and you want to combine this internal data with the event stream data — that you collect via RudderStack or competitors — to create a 360-degree view of the customer and act on that. This becomes very difficult with the SaaS-hosted data model of our competitors — you won’t be sending all your internal data to these cloud vendors.”
Part of its appeal, of course, is the open-source nature of RudderStack, whose GitHub repository now has more than 1,700 stars for the main RudderStack server. Mitra credits getting on the front page of HackerNews for its first sale. On that day, it received over 500 GitHub stars, a few thousand clones and a lot of signups for its hosted app. “One of those signups turned out to be our first paid customer. They were already a competitor’s customer, but it wasn’t scaling up so were looking to build something in-house. That’s when they found us and started working with us,” he said.
Because it is open source, companies can run RudderStack anyway they want, but like most similar open-source companies, RudderStack offers multiple hosting options itself, too, that include cloud hosting, starting at $2,000 per month, with unlimited sources and destination.
Current users include IFTTT, Mattermost, MarineTraffic, Torpedo and Wynn Las Vegas.
As for the Blendo acquisition, it’s worth noting that the company only raised a small amount of money in its seed round. The two companies did not disclose the price of the acquisition.
“With Blendo, I had the opportunity to be part of a great team that executed on the vision of turning any company into a data-driven organization,” said Blendo founder Kostas Pardalis, who has joined RudderStack as head of Growth. “We’ve combined the talented Blendo and RudderStack teams together with the technology that both companies have created, at a time when the customer data market is ripe for the next wave of innovation. I’m excited to help drive RudderStack forward.”
Mitra tells me that RudderStack acquired Blendo instead of building its own version of this technology because “it is not a trivial technology to build — cloud sources are really complicated and have weird schemas and API challenges and it would have taken us a lot of time to figure it out. There are independent large companies doing the ETL piece.”
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Microsoft launches Azure Synapse Link to help enterprises get faster insights from their data
At its Build developer conference, Microsoft today announced Azure Synapse Link, a new enterprise service that allows businesses to analyze their data faster and more efficiently, using an approach that’s generally called “hybrid transaction/analytical processing” (HTAP). That’s a mouthful; it essentially enables enterprises to use the same database system for analytical and transactional workloads on a single system. Traditionally, enterprises had to make some trade-offs between either building a single system for both that was often highly over-provisioned or maintain separate systems for transactional and analytics workloads.
Last year, at its Ignite conference, Microsoft announced Azure Synapse Analytics, an analytics service that combines analytics and data warehousing to create what the company calls “the next evolution of Azure SQL Data Warehouse.” Synapse Analytics brings together data from Microsoft’s services and those from its partners and makes it easier to analyze.
“One of the key things, as we work with our customers on their digital transformation journey, there is an aspect of being data-driven, of being insights-driven as a culture, and a key part of that really is that once you decide there is some amount of information or insights that you need, how quickly are you able to get to that? For us, time to insight and a secondary element, which is the cost it takes, the effort it takes to build these pipelines and maintain them with an end-to-end analytics solution, was a key metric we have been observing for multiple years from our largest enterprise customers,” said Rohan Kumar, Microsoft’s corporate VP for Azure Data.
Synapse Link takes the work Microsoft did on Synaps Analytics a step further by removing the barriers between Azure’s operational databases and Synapse Analytics, so enterprises can immediately get value from the data in those databases without going through a data warehouse first.
“What we are announcing with Synapse Link is the next major step in the same vision that we had around reducing the time to insight,” explained Kumar. “And in this particular case, a long-standing barrier that exists today between operational databases and analytics systems is these complex ETL (extract, transform, load) pipelines that need to be set up just so you can do basic operational reporting or where, in a very transactionally consistent way, you need to move data from your operational system to the analytics system, because you don’t want to impact the performance of the operational system in any way because that’s typically dealing with, depending on the system, millions of transactions per second.”
ETL pipelines, Kumar argued, are typically expensive and hard to build and maintain, yet enterprises are now building new apps — and maybe even line of business mobile apps — where any action that consumers take and that is registered in the operational database is immediately available for predictive analytics, for example.
From the user perspective, enabling this only takes a single click to link the two, while it removes the need for managing additional data pipelines or database resources. That, Kumar said, was always the main goal for Synapse Link. “With a single click, you should be able to enable real-time analytics on your operational data in ways that don’t have any impact on your operational systems, so you’re not using the compute part of your operational system to do the query, you actually have to transform the data into a columnar format, which is more adaptable for analytics, and that’s really what we achieved with Synapse Link.”
Because traditional HTAP systems on-premises typically share their compute resources with the operational database, those systems never quite took off, Kumar argued. In the cloud, with Synapse Link, though, that impact doesn’t exist because you’re dealing with two separate systems. Now, once a transaction gets committed to the operational database, the Synapse Link system transforms the data into a columnar format that is more optimized for the analytics system — and it does so in real time.
For now, Synapse Link is only available in conjunction with Microsoft’s Cosmos DB database. As Kumar told me, that’s because that’s where the company saw the highest demand for this kind of service, but you can expect the company to add support for available in Azure SQL, Azure Database for PostgreSQL and Azure Database for MySQL in the future.
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Fishtown Analytics raises $12.9M Series A for its open-source analytics engineering tool
Philadelphia-based Fishtown Analytics, the company behind the popular open-source data engineering tool dbt, today announced that it has raised a $12.9 million Series A round led by Andreessen Horowitz, with the firm’s general partner Martin Casado joining the company’s board.
“I wrote this blog post in early 2016, essentially saying that analysts needed to work in a fundamentally different way,” Fishtown founder and CEO Tristan Handy told me, when I asked him about how the product came to be. “They needed to work in a way that much more closely mirrored the way the software engineers work and software engineers have been figuring this shit out for years and data analysts are still like sending each other Microsoft Excel docs over email.”
The dbt open-source project forms the basis of this. It allows anyone who can write SQL queries to transform data and then load it into their preferred analytics tools. As such, it sits in-between data warehouses and the tools that load data into them on one end, and specialized analytics tools on the other.
As Casado noted when I talked to him about the investment, data warehouses have now made it affordable for businesses to store all of their data before it is transformed. So what was traditionally “extract, transform, load” (ETL) has now become “extract, load, transform” (ELT). Andreessen Horowitz is already invested in Fivetran, which helps businesses move their data into their warehouses, so it makes sense for the firm to also tackle the other side of this business.
“Dbt is, as far as we can tell, the leading community for transformation and it’s a company we’ve been tracking for at least a year,” Casado said. He also argued that data analysts — unlike data scientists — are not really catered to as a group.
Before this round, Fishtown hadn’t raised a lot of money, even though it has been around for a few years now, except for a small SAFE round from Amplify.
But Handy argued that the company needed this time to prove that it was on to something and build a community. That community now consists of more than 1,700 companies that use the dbt project in some form and over 5,000 people in the dbt Slack community. Fishtown also now has over 250 dbt Cloud customers and the company signed up a number of big enterprise clients earlier this year. With that, the company needed to raise money to expand and also better service its current list of customers.
“We live in Philadelphia. The cost of living is low here and none of us really care to make a quadro-billion dollars, but we do want to answer the question of how do we best serve the community,” Handy said. “And for the first time, in the early part of the year, we were like, holy shit, we can’t keep up with all of the stuff that people need from us.”
The company plans to expand the team from 25 to 50 employees in 2020 and with those, the team plans to improve and expand the product, especially its IDE for data analysts, which Handy admitted could use a bit more polish.
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Databricks makes bringing data into its ‘lakehouse’ easier
Databricks today announced the launch of its new Data Ingestion Network of partners and the launch of its Databricks Ingest service. The idea here is to make it easier for businesses to combine the best of data warehouses and data lakes into a single platform — a concept Databricks likes to call “lakehouse.”
At the core of the company’s lakehouse is Delta Lake, Databricks’ Linux Foundation-managed open-source project that brings a new storage layer to data lakes that helps users manage the lifecycle of their data and ensures data quality through schema enforcement, log records and more. Databricks users can now work with the first five partners in the Ingestion Network — Fivetran, Qlik, Infoworks, StreamSets, Syncsort — to automatically load their data into Delta Lake. To ingest data from these partners, Databricks customers don’t have to set up any triggers or schedules — instead, data automatically flows into Delta Lake.
“Until now, companies have been forced to split up their data into traditional structured data and big data, and use them separately for BI and ML use cases. This results in siloed data in data lakes and data warehouses, slow processing and partial results that are too delayed or too incomplete to be effectively utilized,” says Ali Ghodsi, co-founder and CEO of Databricks. “This is one of the many drivers behind the shift to a Lakehouse paradigm, which aspires to combine the reliability of data warehouses with the scale of data lakes to support every kind of use case. In order for this architecture to work well, it needs to be easy for every type of data to be pulled in. Databricks Ingest is an important step in making that possible.”
Databricks VP of Product Marketing Bharath Gowda also tells me that this will make it easier for businesses to perform analytics on their most recent data and hence be more responsive when new information comes in. He also noted that users will be able to better leverage their structured and unstructured data for building better machine learning models, as well as to perform more traditional analytics on all of their data instead of just a small slice that’s available in their data warehouse.
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Microsoft’s Azure Synapse Analytics bridges the gap between data lakes and warehouses
At its annual Ignite conference in Orlando, Fla., Microsoft today announced a major new Azure service for enterprises: Azure Synapse Analytics, which Microsoft describes as “the next evolution of Azure SQL Data Warehouse.” Like SQL Data Warehouse, it aims to bridge the gap between data warehouses and data lakes, which are often completely separate. Synapse also taps into a wide variety of other Microsoft services, including Power BI and Azure Machine Learning, as well as a partner ecosystem that includes Databricks, Informatica, Accenture, Talend, Attunity, Pragmatic Works and Adatis. It’s also integrated with Apache Spark.
The idea here is that Synapse allows anybody working with data in those disparate places to manage and analyze it from within a single service. It can be used to analyze relational and unstructured data, using standard SQL.
Microsoft also highlights Synapse’s integration with Power BI, its easy to use business intelligence and reporting tool, as well as Azure Machine Learning for building models.
With the Azure Synapse studio, the service provides data professionals with a single workspace for prepping and managing their data, as well as for their big data and AI tasks. There’s also a code-free environment for managing data pipelines.
As Microsoft stresses, businesses that want to adopt Synapse can continue to use their existing workloads in production with Synapse and automatically get all of the benefits of the service. “Businesses can put their data to work much more quickly, productively, and securely, pulling together insights from all data sources, data warehouses, and big data analytics systems,” writes Microsoft CVP of Azure Data, Rohan Kumar.
In a demo at Ignite, Kumar also benchmarked Synapse against Google’s BigQuery. Synapse ran the same query over a petabyte of data in 75% less time. He also noted that Synapse can handle thousands of concurrent users — unlike some of Microsoft’s competitors.
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Incorta raises $30M Series C for ETL-free data processing solution
Incorta, a startup founded by former Oracle executives who want to change the way we process large amounts of data, announced a $30 million Series C today led by Sorenson Capital.
Other investors participating in the round included GV (formerly Google Ventures), Kleiner Perkins, M12 (formerly Microsoft Ventures), Telstra Ventures and Ron Wohl. Today’s investment brings the total raised to $75 million, according to the company.
Incorta CEO and co-founder Osama Elkady says he and his co-founders were compelled to start Incorta because they saw so many companies spending big bucks for data projects that were doomed to fail. “The reason that drove me and three other guys to leave Oracle and start Incorta is because we found out with all the investment that companies were making around data warehousing and implementing advanced projects, very few of these projects succeeded,” Elkady told TechCrunch.
A typical data project involves ETL (extract, transform, load). It’s a process that takes data out of one database, changes the data to make it compatible with the target database and adds it to the target database.
It takes time to do all of that, and Incorta is trying to make access to the data much faster by stripping out this step. Elkady says that this allows customers to make use of the data much more quickly, claiming they are reducing the process from one that took hours to one that takes just seconds. That kind of performance enhancement is garnering attention.
Rob Rueckert, managing director for lead investor Sorenson Capital, sees a company that’s innovating in a mature space. “Incorta is poised to upend the data warehousing market with innovative technology that will end 30 years of archaic and slow data warehouse infrastructure,” he said in a statement.
The company says revenue is growing by leaps and bounds, reporting 284% year over year growth (although they did not share specific numbers). Customers include Starbucks, Shutterfly and Broadcom.
The startup, which launched in 2013, currently has 250 employees, with developers in Egypt and main operations in San Mateo, Calif. They recently also added offices in Chicago, Dubai and Bangalore.
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Snowflake co-founder and president of product Benoit Dageville is coming to TC Sessions: Enterprise
When it comes to a cloud success story, Snowflake checks all the boxes. It’s a SaaS product going after industry giants. It has raised bushels of cash and grown extremely rapidly — and the story is continuing to develop for the cloud data lake company.
In September, Snowflake’s co-founder and president of product Benoit Dageville will join us at our inaugural TechCrunch Sessions: Enterprise event on September 5 in San Francisco.
Dageville founded the company in 2012 with Marcin Zukowski and Thierry Cruanes with a mission to bring the database, a market that had been dominated for decades by Oracle, to the cloud. Later, the company began focusing on data lakes or data warehouses, massive collections of data, which had been previously stored on premises. The idea of moving these elements to the cloud was a pretty radical notion in 2012.
It began by supporting its products on AWS, and more recently expanded to include support for Microsoft Azure and Google Cloud.
The company started raising money shortly after its founding, modestly at first, then much, much faster in huge chunks. Investors included a Silicon Valley who’s who such as Sutter Hill, Redpoint, Altimeter, Iconiq Capital and Sequoia Capital .
Snowflake fund raising by round. Chart: Crunchbase
The most recent rounds came last year, starting with a massive $263 million investment in January. The company went back for more in October with an even larger $450 million round.
It brought on industry veteran Bob Muglia in 2014 to lead it through its initial growth spurt. Muglia left the company earlier this year and was replaced by former ServiceNow chairman and CEO Frank Slootman.
TC Sessions: Enterprise (September 5 at San Francisco’s Yerba Buena Center) will take on the big challenges and promise facing enterprise companies today. TechCrunch’s editors will bring to the stage founders and leaders from established and emerging companies to address rising questions, like the promised revolution from machine learning and AI, intelligent marketing automation and the inevitability of the cloud, as well as the outer reaches of technology, like quantum computing and blockchain.
Tickets are now available for purchase on our website at the early-bird rate of $395.
Student tickets are just $245 – grab them here.
We have a limited number of Startup Demo Packages available for $2,000, which includes four tickets to attend the event.
For each ticket purchased for TC Sessions: Enterprise, you will also be registered for a complimentary Expo Only pass to TechCrunch Disrupt SF on October 2-4.
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