Low-cost and Efficient ETL for Any Data Ecosystem
Extracting, transforming, and loading (ETL) data is a much more complex process than it used to be. Organizations are collecting higher volumes of data from more sources at faster speeds than ever before. Much of this data is useless unless teams can process, store, and analyze it fast enough to inform decision-making. Fortunately, ETL is easy on AWS, no matter the volume, velocity, or variety of incoming data.
At ClearScale, we can help you build data ingestion pipelines, data infrastructure, and ETL processes that set the stage for advanced analytics. Eliminate your ETL bottlenecks, and get more out of your organization’s data with ClearScale and AWS.
“We knew that our company was poised for significant growth and we needed the right partner to help us build a scalable data management platform on AWS. ClearScale designed and implemented a solution to collect and analyze the data generated across our network of IoT devices, allowing us to serve our clients even more effectively.”
- Reza Soudmand, Director of Product Development, Romet Limited
Read Case StudyOur ETL Services
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AWS Glue Implementation
Implement AWS Glue, a serverless data integration service, that simplifies the process of gathering, preparing, and moving data throughout your cloud ecosystem.
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Data Lake Set Up
Set new data lakes up quickly, and automate critical transformations so that your data is ready for analysis.
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Batch Processing
Process vast amounts of data simultaneously for certain use cases, adjusting your ETL workflows as needed to realize your goals.
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Big Data Analytics
After the ETL step, analyze and visualize your data using purpose-built solutions on AWS that make it easy to discover new insights and create value for your organization.
Achieve Your Business Goals with ClearScale and AWS
Lower Data Management Cost
Keep costs down with ETL automation, serverless workflows, and pay-as-you-go pricing on AWS.
Data Processing Flexibility
Use a variety of data processing methods to ensure you get the most out of all data at your disposal.
Big Data Efficiency
Create more capacity for your data engineers and data scientists to study your data in-depth rather than deal with the ingestion and processing pipeline.
