Denodo Platform now available on Google Cloud Platform marketplace offering data delivery through data virtualization

Denodo announces availability of the Denodo Platform through Google Cloud Platform (GCP) Marketplace, making it easier for organizations to transition data to Google Cloud to accelerate cloud data analytics and data lake initiatives. 

As a subscription-based offering, the Denodo Platform on GCP Marketplace provides a true enterprise-scale data management solution for real-time data integration without the cost and complexity associated with traditional data replication. 

As organizations increasingly move data and processing to the cloud, and as data residing across multiple cloud repositories becomes a reality, there is a growing need for faster, more agile data integration methods like data virtualization. 

The Denodo Platform integrates on-premise data sources such as relational databases, data warehouses and CSVs; cloud data sources (Big Query, Cloud Storage, Cloud SQL, Bigtable, Apigee and more), SaaS applications, such as Salesforce, to deliver a standards-based data gateway making it quick and easy for users of all skill levels to access and use data.

Denodo's easy-to-use tools allow data engineers and integrators to quickly prepare integrated data sets for analysts to use in their GCP-based analytical sandbox, while Denodo's dynamic data catalog allows data stewards tag and categorize data assets for easy discovery.

The Denodo Platform delivers accelerated performance by leveraging MPP processing with Spark, Impala, and Presto, in Big Data scenarios, together with a dynamic data catalog so that users can easily find the data that they need. 

Google Cloud customers who deploy the Denodo data virtualization product can realize easy access to a wide breadth of data by connecting, combining and consuming data from a variety of data sources, including Google BigQuery, Google Cloud Storage, and other Google Cloud data sources, to provide a more data-driven approach to advanced real-time analytics, artificial intelligence, and machine learning to help make better business decisions.

The offering also enhances BigQuery support that provides native connectivity and pushdown optimization to enable customers to process massive workloads with better query efficiency. The Denodo Platform’s ability to deliver real-time data access with BigQuery cloud-native APIs enables easy data movement between existing on-premises and cloud data sources and cloud storage.

BigQuery can be leveraged as a high-performance caching database for the Denodo Platform in the cloud. This supports advanced optimization techniques like data movement (data shipping), which enables multi-pass executions based on intermediate temporary tables. Users can leverage utility-based pricing through a number of offerings on the GCP Marketplace. They can start small and scale as needed.

Cloudera Data Platform now available on Microsoft Azure; helps decide on analytic function both in the cloud or on-premises

Cloudera announced on Wednesday that the Cloudera Data Platform (CDP) powered by Microsoft Azure. CDP is an integrated data platform that is easy to secure, manage, and deploy. 

Now available on Azure, it delivers self-service analytics with a consistent Azure experience and enterprise-grade performance across hybrid environments with the granular security and governance policies that IT leaders demand. 

CDP is an integrated data platform that is easy to deploy, manage, and use.  By simplifying operations, CDP reduces the time to onboard new use cases across the organization. It uses machine learning to intelligently autoscale workloads up and down for more cost-effective use of cloud infrastructure.

CDP manages data in any environment, including multiple public clouds, bare metal, private cloud, and hybrid cloud. With Cloudera's Shared Data Experience (SDX), the security and governance capabilities in CDP, IT can confidently deliver secure analytics running against data anywhere. CDP is a new approach to enterprise data, anywhere from the edge to AI.

CDP on Azure includes Cloudera's Shared Data Experience (SDX), which makes it easy to create a secure data lake in hours instead of weeks and replaces tedious scripting with 'set it and forget it' convenience. With CDP on Azure, organizations can speed time to value for data engineering, data warehousing, and machine learning. 

Cloudera leverages Azure infrastructure services such as Azure Data Lake Storage (ADLS) to run high performance analytics with increased availability and durability; Azure Resource Manager (ARM) templates to deploy infrastructure as code in Azure, ensuring rapid, consistent, and repeatable deployment of CDP services; and Azure Kubernetes Service (AKS) to scale up or down depending on the analytics workload to meet service level agreements (SLA) and take advantage of the benefits of cloud bursting.

"For enterprise IT, this means that delivering speed and agility is now just as important as security and governance when it comes to enterprise data management and advanced analytics,” said Mick Hollison, chief marketing officer of Cloudera. “CDP, powered by Microsoft Azure, provides IT with an integrated data platform to work at the speed of business with enterprise-grade security, privacy, and compliance for any type of data."

"Cloudera customers choose Azure because of the freedom to build, manage, and deploy applications on a global, hybrid cloud offering using their favorite tools and frameworks," said Arpan Shah, General Manager, Azure Marketing, Microsoft. "I am excited to have the Cloudera Data Platform available on Azure."

Cloudera Data Platform is available for preview on Azure.

SnapLogic extends alliance with Databricks to boost data lake reliability; analyze big data workloads in the cloud

SnapLogic has expanded its partnership with Databricks with new support for Delta Lake, the open source storage layer created by Databricks that brings reliability to traditional data lakes. 

Together, the joint solution helps customers accelerate the integration, transformation, and processing of big data workloads into Delta Lake, increasing data quality and accelerating the time to value of advanced analytics and machine learning initiatives.

Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. Delta Lake runs on top of existing data lake and is fully compatible with Apache Spark APIs. Delta Lake on Databricks allows users to configure Delta Lake based on their workload patterns and provides optimized layouts and indexes for fast interactive queries.

Delta Lake sits on top of Apache Spark. The format and the compute layer helps to simplify building big data pipelines and increase the overall efficiency of the pipelines.

Organizations are increasingly investing in data lakes to gain actionable insights into their growing data assets. However, the high volume and complexity of data often results in data quality, reliability and performance issues. 

Together, SnapLogic and Databricks are removing these roadblocks by providing a low-code, visual paradigm for data engineers to create and process data pipelines that leverage the full power of Delta Lake — including features like ACID transactions, scalable metadata handling, schema enforcement, and batch and streaming support.

“Databricks and SnapLogic are committed to delivering product innovations that help organizations reduce the time, effort, and skills needed to manage their big data initiatives so they can quickly turn their data into meaningful insights that drive the business forward,” said Craig Stewart, chief technology officer, SnapLogic. “By teaming up with Databricks, we aim to remove the key technical barriers to data lake and big data management so our customers can accelerate their analytics and machine learning initiatives and focus on delivering real business value.”