BrandPosts are written and edited by members of our sponsor community. BrandPosts create an opportunity for an individual sponsor to provide insight and commentary from their point-of-view directly to our audience. The editorial team does not participate in the writing or editing of BrandPosts.
By Joann Starke
Everyone understands that data is your company’s fuel for insights and business innovation. In the “big data” days, it was defined by three V’s: volume, variety, and velocity. In today’s digital world, data is now defined by three D’s: diversity, distribution, and dynamics. But no matter how you define it, one thing is clear—everything about data has changed.
The organizational roles consuming data have changed from business to technical, strategic to tactical, from the front office to the back office. There’s a new generation of data-native workers who rely on data to complete their daily tasks. They work with data and expect to be able to access it from any location. Moreover, the tools and technologies to engineer, govern, protect, and consume data have changed as organizations are looking to consolidate multiple data management tools to accelerate time to insight while simplifying data management.
In response to these challenges, enterprises are deploying DataOps techniques to automate the delivery of data and insights without generating more data.
DataOps amplifies data value
DataOps is a relatively new function designed to deliver agile, scalable, and controllable data workflows with the following benefits:
Democratize data by unlocking it from silos so the entire company has access
Foster speed to insights, allowing data teams and stakeholders to move faster
Enable incisive decision making at every organizational level
Improve productivity for both data teams and data consumers
To be successful, organizations need more than just automation. They need a technology that ingests data and processes it regardless of its location. For this reason, the two hottest topics in data management today are: data integration and data fabric technology.
What is a data fabric?
The term data fabric may be relatively new—but the importance of what it does is not. Data fabric describes a comprehensive way to solve a big challenge — how to integrate all your data into a single, scalable platform.
Data fabric is a semantic layer that sits above data lakes and data warehouses to deliver a consistent foundation to map and deliver enterprise-wide access to a single source of truth. Just as a loom weaves multiple threads into cloth, a modern data fabric weaves any data type or source into a single, enterprise-wide layer that ingests, processes, and stores data once – then makes it available for reuse across multiple use cases.
Data fabric lets organizations process, manage, and analyze almost any amount of data from a multitude of sources, then enables real-time data access to apps and tools using an array of interfaces. Here is a real customer example:
An analyst in Detroit needs to run a query on an exception report generated by a robotic arm in Munich, Germany. To detect anomalies, the data from the robotic arm needs to be consistent with data in a core location, such as a regional data center. Data fabric ensures that the data is available and consistent regardless of where it originates or where it is accessed.
Introducing HPE Ezmeral Data Fabric
HPE Ezmeral Data Fabric is a proven software-defined data store and file system across a wide-variety of large-scale production environments. Its founding vision: to make data-driven applications a reality for your digital enterprise by:
Allowing universal data access across core, edge, and multi-cloud environments for legacy apps, modern analytics, and AI/ML workloads
Supporting any data type, API, and multiple ingest mechanisms — eliminating the need to manually copy data to another system before it can be accessed
Aggregating file information via a global namespace — allowing multiple apps to work together on the same data sets
Enabling distributed metadata, self-healing, and internal load balancing, which reduces trade-offs between scalability, reliability, and performance
Providing built-in mirroring and point-in-time snapshots for business continuity and disaster recovery, as well as validating the work of data engineers and scientists
The only way to deliver meaningful insights is to integrate and connect all data across multiple sources, tools, and technology. A unified data management solution, like HPE Ezmeral Data Fabric, protects your investment in existing apps, processes, and tools by supporting different protocols. This allows in-place data analysis and accelerates time to insight.
About Joann Starke
Joann’s domain knowledge and technical expertise have contributed to the development and marketing of cloud, analytics, and automation solutions. She holds a B.S. in marketing and computer science. Currently she is the subject matter expert for HPE Ezmeral Data Fabric.