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. Schedule a free, no-strings-attached demo to discover how Upsolver can radically simplify data lake ETL in your organization. Data Lake Ingestion Store event streams as optimized Parquet, in a click.
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Lookup Tables add indexing at high cardinality and performance to your data lake. They enable users to index data by a set of keys and then retrieve the results in milliseconds. Lookup Tables leverage break-through compression technology and smart rollups that enable 10XX more data in-memory compared to alternatives. Lookup Tables are stored on S3 as a time-series. Using smart rollups, Upsolver makes it possible to query any time range by any set of keys. Capture real-time behaviour for users and devices, using window aggregations, nested aggregations and time-series aggregations. Read this case study to learn how Upsolver helped ironSource save thousands of engineering hours and cut costs. Discover best practices you need to know in order to optimize your analytics infrastructure for performance. Instantly improve performance and get fresher, more up-to-date data in dashboards built on AWS Athena — all while reducing querying costs.
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Upsolver unlocks the value of your AWS data lake by automating labor-intensive data engineering work. Effortlessly build perfect data pipelines with nothing but a visual interface and the SQL you already know. Natively connect to message brokers and data lakes Upsolver pulls data directly from your Kafka producer, Kinesis topic or existing object storage — simplifying data lake ingestion and ensuring your data lake stays well-irrigated throughout. Autodetect schema-on-read and statistics per field Immediately get insights into your data — schema, statistics, sparse fields and more. Replace blind ETLs or inaccurate guesstimates with a detailed, comprehensive understanding of your data in real-time. Unlock x faster queries in Athena leveraging optimized Apache Parquet, and enable sub-second latency for real-time use cases.
Event Processing Architecture With Upsolver. Product overview. True self-service ETL for cloud data lakes Effortless operations: ingestion, joins, enrichments and structured outputs. Schedule A Demo. Upsolver enabled us to focus on new product features instead of infrastructure and pipelines. Upsolver is the shortest path from streaming to workable data. Upsolver makes big data much easier than it would be if you had to research all the tech it covers, learn how to apply it, implement the same, then deploy it. With Upsolver, we had an operational data lake with demonstrable value to our customers in three weeks.