DataStax, Cloudera, and IBM all into big data as a service pool
On May 24, at the Strata Data conference in London, Cloudera Inc. which is a United States-based software company unveiled a new platform as a service (PaaS) solution called Altus, which could make it easier for businesses to run big data workloads in the cloud.
Cloudera’s new data science tool aims to boost big data and machine learning for businesses!
Key features of Altus:
- Through Altus, data engineers can provision popular open source tools like Apache Spark, Apache Hive, Hive on Spark, and MapReduce2 in a cloud-native environment, the release said.
- Altus also provides default cluster settings for faster deployment time, easier management, and simple automation. It’s now available on Amazon Web Services (AWS), but will eventually make its way to Microsoft Azure.
- Altus works with multiple versions of Cloudera Distributed Hadoop (CDH), and the service also provides built-in workload management to improve troubleshooting, the release said.
- Altus relies on on-demand infrastructure and elastic pipelines to help organizations quickly ramp up their infrastructure as needed for those workloads.
- Altus helps to provide features like metadata, security, management, and common storage “across multiple data engineering applications.”
Not only this company, Santa Clara based DataStax which is the leader in data management for cloud applications launched on May 24,this year ,the DataStax Managed Cloud, a fully managed service of the DataStax Enterprise data platform, which is based upon Apache Cassandra.
Like Cloudera, DataStax is initially launching its service on the Amazon platform with plans to add Microsoft Azure and Google Cloud Platform support in the future.
The announcement fulfills intentions the company stated last fall when it acquired Data Scale Inc., a cloud-based managed service provider.
Key features of DataStax Managed Cloud(Apache Cassandra):
- Focus on business innovation-DataStax Managed Cloud allows you to focus on what matters most – business innovation – by offloading operations to DataStax experts. Reduce risk while easily scaling your data management in a secure environment as your business expands.
- Accelerate time to market-DataStax Managed Cloud simplifies data management – Since you get a single underlying data layer that can run both on-premises and on public clouds, you don’t have to worry about rewriting applications against every cloud provider’s proprietary data services.
- Have choice and flexibility to support hybrid architecture needs-DataStax Managed Cloud extends to fit your hybrid cloud architectures. With 100% compatibility with DataStax Enterprise for your on premise implementations, you can span or migrate across infrastructure models while avoiding cloud vendor lock-in by retaining data autonomy. You can also leverage DataStax Enterprise Operations Service for management of your on-premises or private cloud deployment of our always-on data platform.
IBM’s offering is based upon OpenStack for quick integration with existing hybrid and private clouds, the company said. The package includes a self-service portal that enables users to quickly deploy their choice of open source community databases, a scalable, automated, and reliable open platform for on-premises, private cloud delivery, a disk image builder tool for customers who want to build and deploy their own custom databases to the database image library, an unspecified open source operations manager and a turnkey storage configuration comprised of storage servers, JBOD (just a bunch of disks) disk drawers, OpenStack control plane nodes and network switches pre-integrated with the open source database-as-service toolkit.
The information management big data and analytics capabilities include:
- The information management big data and analytics capabilities include :
Data Management & Warehouse: Gain industry-leading database performance across multiple workloads while lowering administration, storage, development and server costs; Realize extreme speed with capabilities optimized for analytics workloads such as deep analytics, and benefit from workload-optimized systems that can be up and running in hours.
- Hadoop System: Bring the power of Apache Hadoop to the enterprise with application accelerators, analytics, visualization, development tools, performance and security features.
- Stream Computing: Efficiently deliver real-time analytic processing on constantly changing data in motion and enable descriptive and predictive analytics to support real-time decisions. Capture and analyze all data, all the time, just in time. With stream computing, store less, analyze more and make better decisions faster.
- Content Management: Enable comprehensive content lifecycle and document management with cost-effective control of existing and new types of content with scale, security and stability.
- Information Integration & Governance: Build confidence in big data with the ability to integrate, understand, manage and govern data appropriately across its lifecycle.
IBM has clearly made a big investment in building out a powerful Big Data platform!
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