Apache Hadoop as a Service

Apache Hadoop as A Service

The open source project that spawned generations of big-data technologies and provides the foundation for Hive, Pig and MapReduce, Hadoop is still today’s choice for workloads that require virtually unlimited scalability, a high degree of dependability, and support for a wide range of workload types. These characteristics make Apache Hadoop particularly suitable for batch processing of ETL jobs on large data sets, complex workflow diagrams, or data structures that exceed the in-memory limitations of other engines.

A self-managing and self-optimizing implementation of Apache Hadoop

Qubole offers the first Autonomous Data Platform implementation of the open-source Apache Hadoop project.

Runs on your choice of popular public Cloud infrastructure

Runs applications written in MapReduce, Cascading, Pig, Hive, and Scalding

Leverages the platform’s AIR (Alerts, Insights, Recommendations) capabilities to help data teams focus on outcome, instead of the platform

Cloud Agent technology augments original Hadoop with a self-managing and self-optimizing platform:

Cloud-optimized for faster workload performance

Smarter object storage access for split computation, batching of writes, pre-fetching, and multiple caching layers, SSD Caching
Use of Yarn as resource manager allows Hive metastore to be used across engines (Spark, Presto, Hive)

Easier to integrate with existing data sources and tools

ODBC/JDBC drivers
Database connectors (MySQL, SQL Server, Oracle DB, RDS, Redshift, Kinesis and many others)
Exhaustive dictionary of REST APIs for application integration

Best-in-class security

HDFS and SSL encryption
SAML Authentication
VPC support
Dual IAM roles

Supported Versions

AWS: Hadoop 1 0.20.1

AWS, Azure, Oracle OCI: Hadoop 2 2.6.0

WE ARE GROWING VERY FAST AS A STARTUP AND NEEDED A WAY ACCELERATE OUR TIME TO VALUE FOR HADOOP,” EXPLAINS MICKEY ALON, INSIGHTERA’S CEO, AND CO-FOUNDER. “WE WANTED TO FOCUS MORE ON DATA PROCESSING AND TURNING INSIGHTS INTO ACTIONABLE RESULTS, AND LESS ON THE OPERATIONAL SIDE OF HADOOP AND AMAZON S3 FOR TACKLING OUR BIG DATA INTEGRATION CHALLENGES.