Whether you’re a software developer, system or database administrator, or work in sales admin, Data Pipelines can save your business time and money in accessing those key insights. You can simply define and automate your reporting pipelines, using our intuitive UI - meaning there’s no coding skills needed.
If you have basic Excel skills, you’ll find Data Pipelines easy. It takes just a few clicks to define your pipeline. The handy inbuilt scheduler lets you run automated reports at specified times, and using the power of open source industry standard Apache Spark, rapidly outputs them in a variety of file formats and databases. You can deliver reports to Google Sheets, making analysis of your data more accessible, and collaboration across the business easier.
Powered by the open source distributed analytics engine, Apache Spark. No workload is too large.
See the output of your pipeline definition as you add operations step-by-step. The pipeline builder UI ensures the defined process is always valid.
Fully integrated with Google Sheets to deliver reports in a convenient, accessible format for everyone to share.
Seamlessly integrates the most popular data sources in one pipeline (Amazon S3, SQL database, Google Sheets).
Save money on server costs with our serverless model while harnessing the power of distributed computing.
Business challengeA website analytics platform collected URLs in CSV files stored in Amazon S3. However these files were too large to inspect in Excel on a single computer.
SolutionAnalysts took just a few minutes to connect the data using Data Pipelines, and then view it using the graphical interface.
Business challengeMicro transaction metadata belonging to a mobile games developer was stored in Parquet files on Amazon S3. The company needed several aggregated daily performance reports, but written to a MySQL database.
SolutionUsing Data Pipelines, administrators connected their data to our platform in minutes. They could preview each step of the process before scheduling daily pipelines.
Business challengeHistorical medical data was stored by a company on hard drives. They needed processing and migrating to Amazon S3. The company preferred to process the data in-house to save costs.
SolutionWith Data Pipelines’ self-hosting option, the company’s data stored locally and in Amazon S3 was processed on the company's in-house infrastructure then data was then written to S3 buckets.