Store event streams as optimized Parquet, in a click.
Data is stored ready for analytics and ML
No coding or IT resources required
Understand your data upon ingest
To understand the value of using a tool such as Upsolver for ingest pipelines, we need to understand the types of challenges most organizations encounter when writing data to object storage such as Amazon S3. These include:
Various open-source frameworks can be used to ingest big data into object storage such as Amazon S3, Azure Blob or on-premises Hadoop. However, these tend to be very developer-centric and can be taxing to configure and maintain, especially when new data sources or schemas are being added or when data volumes grow very quickly. In these cases, automating data ingestion could prove to be a more robust and reliable solution.
Upsolver automates ingestion by natively connecting to event streams (Apache Kafka or Amazon Kinesis) or existing object storage, stores a raw copy of the data for lineage and replay, along with consumption-ready Parquet data – including automatic partitioning, compaction and compression. Once data is on S3, Upsolver offers industry-leading integration with the Glue Data Catalog, making your data instantly available in query engines such as Athena, Presto, Qubole or Spark.
The key benefits we hear from customers who replaced coding-based data ingestion with Upsolver’s automated data lake ETL tool include: