Amazon does it all in cloud

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Amazon does it all in cloud Analytical DBMS: Amazon Redshift service (based on ParAccel engine); Amazon Relational Database Service. In-memory DBMS: None. Third-party options on AWS include Altibase, SAP Hana, and ScaleOut. Hadoop distributions: Amazon Elastic MapReduce. Third-party options include Cloudera and MapR. Stream-processing technology: Amazon Kinesis. Hardware/software systems: Not applicable. Amazon Web Services hosts a who's who list of data-management services from third-party players -- Cloudera, Microsoft, Oracle, SAP, and many others -- but the cloud giant has its own long-term ambitions where big-data analysis is concerned. Building on its Elastic Compute Cloud (EC2) and Simple Storage Service (S3) storage infrastructure, Amazon launched its Hadoop-based Elastic MapReduce service way back in 2009. In 2013, AWS added the Redshift Data Warehousing service (based on the ParAccel DBMS), which is supported by another who's who list of independent data-integration, business intelligence, and analytics vendors. Rounding out AWS's big-data capabilities are the DynamoDB NoSQL database management service and Kinesis Stream Processing service. Amazon's biggest appeal is clearly to organizations running data-intensive applications on its cloud. That said, leading Hadoop distributor Cloudera recently partnered with AWS, apparently reasoning that many enterprises are choosing hybrid strategies in which they're moving selected big-data workloads into the cloud while keeping sensitive data and mission-critical workloads on-premises. Look for AWS to exploit this opportunity by adding even more options to connect with enterprise data centers.

Amazon does it all in cloud

Analytical DBMS: Amazon Redshift service (based on ParAccel engine); Amazon Relational Database Service.
In-memory DBMS: None. Third-party options on AWS include Altibase, SAP Hana, and ScaleOut.
Hadoop distributions: Amazon Elastic MapReduce. Third-party options include Cloudera and MapR.
Stream-processing technology: Amazon Kinesis.
Hardware/software systems: Not applicable.
Amazon Web Services hosts a who's who list of data-management services from third-party players -- Cloudera, Microsoft, Oracle, SAP, and many others -- but the cloud giant has its own long-term ambitions where big-data analysis is concerned. Building on its Elastic Compute Cloud (EC2) and Simple Storage Service (S3) storage infrastructure, Amazon launched its Hadoop-based Elastic MapReduce service way back in 2009. In 2013, AWS added the Redshift Data Warehousing service (based on the ParAccel DBMS), which is supported by another who's who list of independent data-integration, business intelligence, and analytics vendors. Rounding out AWS's big-data capabilities are the DynamoDB NoSQL database management service and Kinesis Stream Processing service.
Amazon's biggest appeal is clearly to organizations running data-intensive applications on its cloud. That said, leading Hadoop distributor Cloudera recently partnered with AWS, apparently reasoning that many enterprises are choosing hybrid strategies in which they're moving selected big-data workloads into the cloud while keeping sensitive data and mission-critical workloads on-premises. Look for AWS to exploit this opportunity by adding even more options to connect with enterprise data centers.

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