Apache Lens is a unified Analytics Platform. Lens aims to cut the Data Analytics silos by providing a single view of data across multiple tiered data stores and optimal execution environment for the analytical query. It is an OLAP virtualization platform that provides multi-dimensional queries in a unified way over data-sets stored in multiple data stores. A single Cube in Lens can span across multiple query engines like Apache Hive, Spark, ElasticSearch and traditional data warehouses.
This allows users to make cost performance trade-offs for different kind of workloads on batch or real-time data. Real-time data can be fully materialized in a fast mutable store like ElasticSearch; and large but older data that is less frequently accessed can be stored in a batch store like Hive in star or snowflake schema. Lens allows users to be agnostic to the underlying physical tables and work with the data cube with a SQL like query language. It provides query life-cycle management and query performance statistics across multiple engines. It also comes with a User Interface to browse schema and run and track queries.
Like traditional OLAP systems, Lens understands the primitives of facts and dimensions. Facts are immutable, time-series data of actual events and comprise of dim-attributes and measures. Dim-attributes are the different domain attributes by which data needs to be analysed. Lens also separates the query interface for facts and dimensions from the actual storage. Queries are fired against the Lens ‘Cube’ and ‘Dimension’ objects. Actual data is stored in abstractions called ‘fact tables’ and ‘dim tables’. Several ‘fact tables’ can be associated with the Cube. Similarly, several ‘dim tables’ can be associated with the Dimension. When the user queries for data against a cube, Lens provides the intelligence for picking up the right fact table.
Apart from the above mentioned features, Lens also has the following advantages as a standalone platform:
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