HOME Kyuubi™ is a unified multi-tenant JDBC interface for large-scale data processing and analytics, built on top of Apache Spark™ [http://spark.apache.org/]. In general, the complete ecosystem of Kyuubi apache.org/] to build and manage Data Lake with pure SQL for both data processing e.g. ETL, and analytics e.g. BI. All workloads can be done on one platform, using one copy of data, with one SQL interface cluster managers. High Performance Kyuubi is built on the Apache Spark, a lightning-fast unified analytics engine. Concurrent execution: multiple Spark applications work together Quick response: long-running

HOME Kyuubi™ is a unified multi-tenant JDBC interface for large-scale data processing and analytics, built on top of Apache Spark™ [http://spark.apache.org/]. In general, the complete ecosystem of Kyuubi apache.org/] to build and manage Data Lake with pure SQL for both data processing e.g. ETL, and analytics e.g. BI. All workloads can be done on one platform, using one copy of data, with one SQL interface cluster managers. High Performance Kyuubi is built on the Apache Spark, a lightning-fast unified analytics engine. Concurrent execution: multiple Spark applications work together Quick response: long-running

to build and manage Data Lakehouse with pure SQL for both data processing, e.g. ETL, and online analytics processing(OLAP), e.g. BI. All workloads can be done on one platform, using one copy of data, with text) in your data lake in cloud storage or an on-prem HDFS cluster. • Lakehouse formation and analytics – Easily build an ACID table storage layer via Hudi, Iceberg, or/and Delta Lake. • Logical data if could. Why do we need this feature? Apache Spark is a unified engine for large-scale data analytics. Using Spark to process data is like driving an all-wheel- drive hefty horsepower supercar. However

to build and manage Data Lakehouse with pure SQL for both data processing, e.g. ETL, and online analytics processing(OLAP), e.g. BI. All workloads can be done on one platform, using one copy of data, with text) in your data lake in cloud storage or an on-prem HDFS cluster. • Lakehouse formation and analytics – Easily build an ACID table storage layer via Hudi, Iceberg, or/and Delta Lake. • Logical data if could. Why do we need this feature? Apache Spark is a unified engine for large-scale data analytics. Using Spark to process data is like driving an all-wheel- drive hefty horsepower supercar. However

to build and manage Data Lakehouse with pure SQL for both data processing, e.g. ETL, and online analytics processing(OLAP), e.g. BI. All workloads can be done on one platform, using one copy of data, with text) in your data lake in cloud storage or an on-prem HDFS cluster. • Lakehouse formation and analytics – Easily build an ACID table storage layer via Hudi, Iceberg, or/and Delta Lake. • Logical data if could. Why do we need this feature? Apache Spark is a unified engine for large-scale data analytics. Using Spark to process data is like driving an all-wheel- drive hefty horsepower supercar. However

to build and manage Data Lakehouse with pure SQL for both data processing, e.g. ETL, and online analytics processing(OLAP), e.g. BI. All workloads can be done on one platform, using one copy of data, with text) in your data lake in cloud storage or an on-prem HDFS cluster. • Lakehouse formation and analytics – Easily build an ACID table storage layer via Hudi, Iceberg, or/and Delta Lake. • Logical data if could. Why do we need this feature? Apache Spark is a unified engine for large-scale data analytics. Using Spark to process data is like driving an all-wheel- drive hefty horsepower supercar. However

to build and manage Data Lakehouse with pure SQL for both data processing, e.g. ETL, and online analytics processing(OLAP), e.g. BI. All workloads can be done on one platform, using one copy of data, with text) in your data lake in cloud storage or an on-prem HDFS cluster. • Lakehouse formation and analytics – Easily build an ACID table storage layer via Hudi, Iceberg, or/and Delta Lake. • Logical data if could. Why do we need this feature? Apache Spark is a unified engine for large-scale data analytics. Using Spark to process data is like driving an all-wheel- drive hefty horsepower supercar. However

to build and manage Data Lakehouse with pure SQL for both data processing, e.g. ETL, and online analytics processing(OLAP), e.g. BI. All workloads can be done on one platform, using one copy of data, with text) in your data lake in cloud storage or an on-prem HDFS cluster. • Lakehouse formation and analytics – Easily build an ACID table storage layer via Hudi, Iceberg, Delta Lake or/and Paimon. • Logical 0-SNAPSHOT Why do we need this feature? Apache Spark is a unified engine for large-scale data analytics. Using Spark to process data is like driving an all-wheel- drive hefty horsepower supercar. However

to build and manage Data Lakehouse with pure SQL for both data processing, e.g. ETL, and online analytics processing(OLAP), e.g. BI. All workloads can be done on one platform, using one copy of data, with text) in your data lake in cloud storage or an on-prem HDFS cluster. • Lakehouse formation and analytics – Easily build an ACID table storage layer via Hudi, Iceberg, or/and Delta Lake. • Logical data Release 1.8.0 Why do we need this feature? Apache Spark is a unified engine for large-scale data analytics. Using Spark to process data is like driving an all-wheel- drive hefty horsepower supercar. However

to build and manage Data Lakehouse with pure SQL for both data processing, e.g. ETL, and online analytics processing(OLAP), e.g. BI. All workloads can be done on one platform, using one copy of data, with text) in your data lake in cloud storage or an on-prem HDFS cluster. • Lakehouse formation and analytics – Easily build an ACID table storage layer via Hudi, Iceberg, or/and Delta Lake. • Logical data Release 1.8.0 Why do we need this feature? Apache Spark is a unified engine for large-scale data analytics. Using Spark to process data is like driving an all-wheel- drive hefty horsepower supercar. However