Cloudera, the modern platform for machine learning and analytics optimised for the cloud, has announced the beta release of Cloudera Altus Data Engineering for the Microsoft Azure cloud platform. According to Cloudera the cloud is one of the fastest-growing deployment environments for Cloudera customers, and this alliance offers businesses a proven data engineering solution for Azure.
Cloudera Altus, a platform-as-a-service (PaaS) offering built on the enterprise-grade Cloudera distribution, helps data engineers use on-demand cloud infrastructure to speed the creation and operation of data pipelines that power sophisticated, data-driven applications. Altus on Azure will provide an easy, unified, and enterprise-ready data engineering experience, allowing end users greater choice in cloud infrastructure providers.
Cloudera Altus Data Engineering on Azure simplifies the development and operations of data pipelines, focusing on data engineering workloads, while abstracting infrastructure management and operations that can be both time consuming and complex. Customers are able to host their data lakes on Azure Data Lake Store (ADLS), the hyperscale cloud storage system for data analytics, and use on-demand Azure infrastructure capacity for end user self-service capability. By separating computation and storage, ADLS is able to scale resources independently for demanding customer use cases.
Because Altus is backed by the enterprise-grade Cloudera distribution, Altus reduces the risk associated with cloud migrations and workload migrations across cloud providers. Altus provides users with familiar tools and delivers shared data storage and metadata management across data pipelines.
According to KPMG, PaaS adoption is predicted to be the fastest-growing sector of cloud platforms, growing from 32% in 2017 to 56% adoption in 2020. In addition, cloud adoption is now mainstream and accelerating as enterprises shift data-intensive operations to the cloud.
“Cloudera makes it easy, cost-effective, and convenient to deploy data analytic workloads on cloud providers, like Microsoft Azure, taking advantage of cloud elasticity, low-cost storage and compute options, and rapid provisioning,” said Charles Zedlewski, SVP, Product Management, at Cloudera. “We are focused on providing a service that interoperates with our complete stack to deliver a superior experience in the cloud.”
“As the leader in enterprise cloud data management, Informatica continues to invest in helping organisations easily deploy data lakes in the cloud. Informatica offers a comprehensive suite of data management products certified to run on Microsoft Azure,” said Ronen Schwartz, SVP and GM of Cloud, Big Data, and Data Integration.
“Enterprise customers increasingly choose Microsoft Azure for their large-scale data processing workloads. We are excited that Cloudera Altus will bring an easy-to-use, end-user focused managed service experience on Azure, that is backed by the proven enterprise-grade Cloudera distribution,” said Corey Sanders, Director of Compute at Microsoft Azure. “Azure is the only public cloud that provides Azure Data Lake Storage designed for big data at cloud scale. Together with Cloudera Altus, we help customers build, deploy, and share analytics solutions.”
Features and benefits of Altus Data Engineering for Azure include:
●Workload orientation
Altus is focused on building data pipelines rather than administering clusters or infrastructure, so users can easily submit, clone, and troubleshoot pipelines with minimal attention paid to the underlying infrastructure.
●No data siloes
Altus enables data engineers to run data processing jobs that directly read from and write to ADLS. This data is immediately available for use by other Cloudera compute engines without requiring data replication, extract, transform and load (ETL) activities or changes to file formats, and eliminates the overhead costs of storing the same data multiple times over.
●Integration with popular third-party tools
●Built-in workload analytics
Workload analytics allows users to easily troubleshoot failed jobs. In addition, Altus workload analytics can flag workload performance deviations and perform root cause analysis.
Today, Cloudera Altus Data Engineering includes support for Apache Spark, Apache Hive, and Hive on Spark, and MapReduce2.
This collaboration is the latest of several between Cloudera and Microsoft. In addition to Altus, business analysts can ingest sensor data through the Azure IoT Hub, pull Cloudera-processed data into Microsoft SQL Server via Microsoft Polybase, and visualise insights using Microsoft Power BI for Apache's Impala. Through this integration with Microsoft, Cloudera offers businesses machine learning tools that can support large scale data analysis and predictive analytics cost-efficiently.
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Join the Cloudera Community
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Cloudera Altus, a platform-as-a-service (PaaS) offering built on the enterprise-grade Cloudera distribution, helps data engineers use on-demand cloud infrastructure to speed the creation and operation of data pipelines that power sophisticated, data-driven applications. Altus on Azure will provide an easy, unified, and enterprise-ready data engineering experience, allowing end users greater choice in cloud infrastructure providers.
Cloudera Altus Data Engineering on Azure simplifies the development and operations of data pipelines, focusing on data engineering workloads, while abstracting infrastructure management and operations that can be both time consuming and complex. Customers are able to host their data lakes on Azure Data Lake Store (ADLS), the hyperscale cloud storage system for data analytics, and use on-demand Azure infrastructure capacity for end user self-service capability. By separating computation and storage, ADLS is able to scale resources independently for demanding customer use cases.
Because Altus is backed by the enterprise-grade Cloudera distribution, Altus reduces the risk associated with cloud migrations and workload migrations across cloud providers. Altus provides users with familiar tools and delivers shared data storage and metadata management across data pipelines.
According to KPMG, PaaS adoption is predicted to be the fastest-growing sector of cloud platforms, growing from 32% in 2017 to 56% adoption in 2020. In addition, cloud adoption is now mainstream and accelerating as enterprises shift data-intensive operations to the cloud.
“Cloudera makes it easy, cost-effective, and convenient to deploy data analytic workloads on cloud providers, like Microsoft Azure, taking advantage of cloud elasticity, low-cost storage and compute options, and rapid provisioning,” said Charles Zedlewski, SVP, Product Management, at Cloudera. “We are focused on providing a service that interoperates with our complete stack to deliver a superior experience in the cloud.”
“As the leader in enterprise cloud data management, Informatica continues to invest in helping organisations easily deploy data lakes in the cloud. Informatica offers a comprehensive suite of data management products certified to run on Microsoft Azure,” said Ronen Schwartz, SVP and GM of Cloud, Big Data, and Data Integration.
“Enterprise customers increasingly choose Microsoft Azure for their large-scale data processing workloads. We are excited that Cloudera Altus will bring an easy-to-use, end-user focused managed service experience on Azure, that is backed by the proven enterprise-grade Cloudera distribution,” said Corey Sanders, Director of Compute at Microsoft Azure. “Azure is the only public cloud that provides Azure Data Lake Storage designed for big data at cloud scale. Together with Cloudera Altus, we help customers build, deploy, and share analytics solutions.”
Features and benefits of Altus Data Engineering for Azure include:
●Workload orientation
Altus is focused on building data pipelines rather than administering clusters or infrastructure, so users can easily submit, clone, and troubleshoot pipelines with minimal attention paid to the underlying infrastructure.
●No data siloes
Altus enables data engineers to run data processing jobs that directly read from and write to ADLS. This data is immediately available for use by other Cloudera compute engines without requiring data replication, extract, transform and load (ETL) activities or changes to file formats, and eliminates the overhead costs of storing the same data multiple times over.
●Integration with popular third-party tools
●Built-in workload analytics
Workload analytics allows users to easily troubleshoot failed jobs. In addition, Altus workload analytics can flag workload performance deviations and perform root cause analysis.
Today, Cloudera Altus Data Engineering includes support for Apache Spark, Apache Hive, and Hive on Spark, and MapReduce2.
This collaboration is the latest of several between Cloudera and Microsoft. In addition to Altus, business analysts can ingest sensor data through the Azure IoT Hub, pull Cloudera-processed data into Microsoft SQL Server via Microsoft Polybase, and visualise insights using Microsoft Power BI for Apache's Impala. Through this integration with Microsoft, Cloudera offers businesses machine learning tools that can support large scale data analysis and predictive analytics cost-efficiently.
Interested?
Join the Cloudera Community
Read about customers' successes
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