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Upsolver Announces SQL-based ETL (Extract, Transform, Load) for Cloud Data Lakes to Democratize Big Data
New data lake ETL platform makes machine learning and big data analytics possible for more organizations by replacing arcane data pipeline coding using Spark/Hadoop with simple SQL.
Sunnyvale, Calif., 16 October 2019 – Upsolver, a rapidly growing big data startup and an Advanced Technology Partner in the Amazon Web Services (AWS) Partner Network (APN), has released SQL-based ETL for cloud data lakes.
The new functionality eliminates friction and complexity in big data initiatives, such as machine learning (ML) and real-time stream processing, lowering the barriers to entry and reducing time toproduction of data lake ETL projects by 95 percent.
Upsolver’s SQL-based data preparation replaces thousands of lines of complex coding by big data engineers with familiar syntax and a visual interface, giving data scientists and analysts unprecedented ability to generate new insights from data lakes.
“Upsolver has allowed me to do things that would otherwise require at least three dedicated engineers,” said Guy Boyangu, CTO at Sisense. “It has been a game-changer in terms of our ability to deliver advanced analytics and has dramatically accelerated our time to market.”
The release will serve to further bolster Upsolver’s cloud platform, which is already used by hundreds of data professionals worldwide to manage their organizational data lakes and transform petabytes of semi-structured data into usable datasets for analytics and ML.
Data lake engineering has long been seen as the main roadblock to cloud data lake adoption. While on-premises Hadoop implementations have fallen out of favor as companies gravitate toward managed cloud storage solutions such as Amazon Simple Storage Service (Amazon S3), most organizations still struggle to see real value from their data lake initiatives due to the challenging nature of ingesting, managing, and preparing high volumes of structured and semi-structured data.
“Our customers are increasingly leveraging solutions like Amazon Athena and data lakes on Amazon S3 to operationalize data at a massive scale” said Rahul Pathak, General Manager, Big Data, Data Lakes, Blockchain at Amazon Web Services, Inc. “We’re delighted to be working with Upsolver to help our customers easily process and analyze high volumes of data.”
Upsolver emerged from stealth early last year with the mission of making data lakes a reality for more organizations.
To realize this vision, founders Ori Rafael and Yoni Eini, both former leaders in one of the Israeli military’s elite technology units, have leveraged stream processing and indexing technology, which the company developed from the ground-up, to provide a frictionless alternative to data lake ETL as compared to Spark/Hadoop.
Since its first release in 2018, the company’s namesake platform has seen rapid adoption in data-intensive organizations such as Sisense, IronSource and The Meet Group (NASDAQ: MEET).
“There is a worldwide shortage of skilled big data engineers,” said Ori Rafael, Co-founder and CEO at Upsolver. “It’s very difficult to find people who are very well-versed in Spark, Airflow, and Cassandra, but every developer and data analyst knows SQL. Our platform empowers anyone to be self-sufficient with big data and frees up valuable time and resources for IT and data engineering departments.”
To learn more about Upsolver, visit www.upsolver.com.
Upsolver is the data lake ETL platform: a single platform to prepare streaming and historical big data for analysis using a visual platform and SQL, at data lake scale. Built from the ground-up for cloud data lakes, Upsolver offers strong integration with popular stream processing and analytics tools including Apache Kafka, Amazon Kinesis, Athena, and more. Upsolver powers data lakes for data-intensive companies such as Sisense, ironSource, and SnapAv, saving thousands of engineering hours while providing up 100x improvement in performance and significantly reducing costs.