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 the data in various formats (Parquet, CSV, JSON, 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 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 the data in various formats (Parquet, CSV, JSON, 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 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 the data in various formats (Parquet, CSV, JSON, 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 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 the data in various formats (Parquet, CSV, JSON, 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 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 the data in various formats (Parquet, CSV, JSON, 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 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 the data in various formats (Parquet, CSV, JSON, 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 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 the data in various formats (Parquet, CSV, JSON, 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 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 the data in various formats (Parquet, CSV, JSON, 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 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 the data in various formats (Parquet, CSV, JSON, 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 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 the data in various formats (Parquet, CSV, JSON, 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 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