Is SQL-based ETL Democratizing Cloud Data Lakes?
The new data lake ETL platform is eradicating the friction and complexities in big data initiatives thereby enhancing business performance and reducing costs.
FREMONT, CA: A swiftly growing big data startup and an Advanced Technology Partner in the Amazon Web Services (AWS) Partner Network (APN), Upsolver, launches SQL-based ETL for cloud data lakes. It eradicates friction and sophistication in big data initiatives like machine learning and real-time stream processing that reduces the barriers to entry, hence mitigating time to production of data lake ETL projects by 95 percent. Upsolver's Data Lake Platform takes the complexities out of streaming data integration, management, and preparation on cloud data lakes like Google Cloud, Azure, or AWS. The company annihilates the necessity to glue together various elements to process, store, and consume streaming data, cutting down the time and cost of big data projects.
The SQL-based ETL serves to sustain Upsolver's cloud platform, used by hundreds of data experts to manage their organizational data lakes globally. This helps experts transform petabytes of semi-structured data into worthy datasets for machine learning 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 dropped out of favor as companies move toward managed cloud storage solutions such as Amazon Simple Storage Service (Amazon S3). Many businesses still strive 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 a data lake ETL solution provider, preparing streaming and historical data for analysis with the use of a visual platform and SQL at a data lake scale. The company provides strong integration with modern stream processing and analytics tools, developed from the ground-up for cloud data lakes. Upsolver powers data lakes for data-intensive businesses, saving thousands of engineering hours while giving up 100x enhancement in performance and significantly mitigating costs.
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