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Mach5 on Databricks

Bring interactive, real-time search over your Lakehouse

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How it works

Indexes are built and are persisted in cheap, cloud storage. The indexes avoid completely duplicating your data by pointing back at your table's base data stored in parquet format - the only data stored by Mach5 is what is required for indexing.

Mach5 indexes data from your databricks delta tables by using your databricks compute clusters.

Mach5's stateless search layer is built as a kubernetes application that serves queries over the data residing in cloud storage.

Features
Interactively find content across your Lakehouse

Simplify your searches and barriers to analyzing your data. Know roughly what you need to find but not sure where to look? Just type in what you are looking for. Mach5's indexes intelligently search across your database to find matches, regardless of which table they live in and regardless where in the data model/schema they are stored.

Auto-scaling and low-cost

Mach5 is built from the ground-up using a cloud-native architecture. Mach5 separates the major functions of a modern search and indexing engine, allowing them to be run in a scalable manner. Mach5 uses databricks compute (Spark job) for performing indexing. Mach5's serving layer is a simple, stateless kubernetes application that automatically scales up and down based on your query workloads. This architecture ultimately results in low TCO.

Benefit from improved performance through indexing

Databricks is optimized for scan-based workloads, crunching high volumes of data. Databricks is less-efficient at needle-in-a-haystack style queries. Mach5 bridges this gap and allows you to run fast, interactive searches over your entire Lakehouse by indexing your data.

Sign up today to try Mach5 on Databricks

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