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Apache Druid is an open-source real-time analytics database designed for fast slice-and-dice analytics on large datasets. It combines the strengths of a time-series database, a search system, and a column-oriented data store, making it suitable for sub-second OLAP queries on streaming data ingestion.
Apache Pinot is a real-time distributed OLAP datastore built to deliver scalable real-time analytics with low latency on streaming and batch data. Originally developed at LinkedIn and donated to the ASF, Pinot powers user-facing analytics on platforms with billions of events per day, including LinkedIn, Uber, Stripe, and Walmart.
Apache Spark is an open-source unified analytics engine for large-scale data processing with built-in modules for SQL, streaming, machine learning, and graph processing. Created at UC Berkeley in 2009, Spark replaced Hadoop MapReduce as the dominant batch processing engine and underpins commercial platforms including Databricks, AWS EMR, and Google Dataproc.
New Imply Lumi customer story, out now: How BTG Pactual Scales Security Investigations Without Replacing Splunk Decouple your observability/security tools Store more data, support more use cases, and spend less with an Observability Warehouse Request a Demo What’s an Observability Warehouse? A new data layer for a faster, cheaper, and more open stack. Tightly coupled […]
Materialize lets your team use SQL to create up-to-the-second views into any element of your business, helping you operate confidently in dynamic environments. Use Materialize to deliver fresh context to AI agents, power data-intensive UIs, and create low-latency, event-driven architectures that drive core business processes.
Enterprise analytics platform combining on-premises and cloud data warehouse capabilities.