Implementing Lakehouse Architecture with Presto at Bolt – Kostiantyn Tsykulenko, Bolt.eu

Implementing Lakehouse Architecture with Presto at Bolt – Kostiantyn Tsykulenko, Bolt.eu

Bolt.eu is the first European mobility super-app. We have over 100M users across Europe and Africa and have to deal with data at a large scale on a daily basis (over 100k queries daily). Previously we were using a traditional data warehouse solution based on Redshift but we’ve faced scalability issues that were hard to overcome and after doing our research we chose Presto as the solution. In just a single year we’ve managed to migrate to the Lakehouse architecture using AWS, Presto, Spark and Delta lake. We would like to talk about our journey, some of the challenges we’ve encountered and how we solved them.

Parquet Column Level Access Control with Presto

Parquet Column Level Access Control with Presto

Apache Parquet is the major columnar file storage format used by Apache Presto and several other query engines in many big data analytic frameworks today. In a lot of use cases, a portion of the column data is highly sensitive and must be protected. Column encryption at the file format level is supported in the Parquet community. Due to the rewritten code of Parquet in Presto, Parquet column encryption at Presto needs to be ported with modifications to the Presto code page. And the integration with Key Management Service (KMS) and other query engines like Hive and Spark is another challenge. In this talk, we will show the work we have done for enabling Presto for Parquet column decryption including challenges, solutions, integration with Hive/Spark Parquet column encryption and look forward to the next step of encryption work.

Speed Up Presto Reading with Paquet Column Indexes – Xinli Shang, & Chen Liang, Uber

Speed Up Presto Reading with Paquet Column Indexes – Xinli Shang, & Chen Liang, Uber

Data analytic tables in the big data ecosystem are usually large and some of them can reach petabytes in size. Presto as a fast query engine needs to be intelligent to skip reading unnecessary data based on filters. In addition to the existing filtering to skip partitions, files, and row groups, Apache Parquet Column Index provides further filtering to pages, which is the I/O unit for the Parquet data source. In this presentation, we will show the work that we integrated Parquet Column Index to Presto code base, the performance gains, etc. We will also talk about our effort to open-source this project to PrestoDB and look forward to collaborating with the community to merge!

Delta Lake Connector for Presto – Denny Lee, Databricks

Delta Lake Connector for Presto – Denny Lee, Databricks

Delta lake is an open-source project that enables building a lakehouse architecture on top of existing storage systems such as S3, ADLS, GCS, and HDFS. We – the Presto and Delta Lake communities – have come together to make it easier for Presto to leverage the reliability of data lakes by integrating with Delta Lake. In this session, we would like to share the design decisions and internals of the Presto/Delta connector.