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How SQL-based ETL Optimizes Cloud Data Management
New data lake ETL platform is making ML and big data analytics possible for organizations by replacing arcane data pipeline coding using Hadoop with simple SQL.
FREMONT, CA: A rapidly growing big data startup and an Advanced Technology Partner in the Amazon Web Services (AWS) Partner Network (APN), Upsolver, has released SQL-based ETL for cloud data lakes. It eliminates friction and complexity in big data initiatives like Machine Learning (ML) and real-time stream processing that lowers the barriers to entry, hence reducing time to production of data lake ETL projects by 95%. Upsolver's Data Lake Platform takes the complications out of streaming data integration, management, and preparation on cloud data lakes like Azure, AWS, or Google Cloud. The company eradicates the need to glue together various components to process, store, and consume streaming data, cutting down the time-to-value and cost of big data projects.
The SQL-based ETL serves to bolster Upsolver's cloud platform, used by hundreds of data professionals globally to manage their organizational data lakes. This helps professionals transform petabytes of semi-structured data into valuable datasets for ML and analytics. Data lake engineering has been seen as the main roadblock to cloud data lake adoption for a long time. On-premises Hadoop implementations have fallen out of favor as organizations move toward managed cloud storage solutions such as Amazon Simple Storage Service (Amazon S3). Many organizations still struggle to see real value in their data lake initiatives due to the challenging nature of ingesting, managing, and preparing high volumes of structured and semi-structured data.
Upsolver is the data lake ETL platform, a single platform that prepares streaming and historical data for analysis using a visual platform and SQL at a data lake scale. The company offers strong integration with popular stream processing and analytics tools, built from the ground-up for cloud data lakes. Upsolver powers data lakes for data-intensive companies saving thousands of engineering hours while providing up 100x improvement in performance and significantly reducing costs.