Overview¶

Starting with the update 8.0u20230301, Denodo has embedded its own MPP engine, the Denodo Lakehouse Accelerator, based on Presto.

Presto is a high performance, distributed SQL query engine for big data. It was originally created by Facebook to provide self-service analytics on top of their massive data sets, and now is open source.

The main goal of the Denodo Lakehouse Accelerator is to provide efficient access to Data Lake content: Parquet files, Iceberg and Delta Lake tables, in an easy way, using only SQL.

Also, with the Denodo Lakehouse Accelerator there is no need for additional external engines. The use of an Lakehouse parallel engine and Object Storage brings out-of-the-box options for those Denodo capabilities that require storage, such as, caching, query acceleration, remote tables, summaries, etc.

The main steps to use the Denodo Lakehouse Accelerator are:

1. Store your dataset in the Object Storage. The Denodo Lakehouse Accelerator can read data from many distributed storage systems, such as:

   • Amazon S3

   • S3-compatible storage

   • Azure Data Lake Storage Gen2

   • Google Cloud Storage

   • Hadoop Distributed File System (HDFS)

2. Deploy the Denodo Lakehouse Accelerator on Kubernetes using the Helm chart, registering it as a data source in the Denodo Platform.

3. Graphically explore the data stored in Parquet files, Iceberg or Delta Lake tables in your Object Storage to create the corresponding tables in the Lakehouse Accelerator and the base views in Denodo.

4. Query your Data Lake in Denodo leveraging the massive parallel processing capabilities of the Denodo Lakehouse Accelerator.

5. Load data, using Parquet file format, in your Data Lake using Denodo capabilities like caching, remote tables or summaries.