In an ideal world, data exists within a single, comprehensive framework that empowers data agility, speed, trust, and ease of access. Standing in the way of this technological haven are legacy architectures, where data is a siloed, unreliable inhibitor of enterprise success. Data fabric architectures aim to dismantle these archaic systems to maximize data value once and for all, yet there are no out-of-the-box, one-size-fits-all solutions for its implementation.
To examine enabling technologies and best practices for adopting a data fabric architecture, experts from TimeXtender and Acceldata joined DBTA’s webinar, Moving to a Data Fabric: Key Challenges and Enabling Technologies, offering an amalgamation of strategies for navigating this otherwise elusive—yet vitally modern—infrastructure.
Micah Horner, product marketing manager at TimeXtender, emphasized that though an unpopular topic, through the popularity of data fabrics, “metadata…is one of the key enabling technologies that makes all of this work.”
Enterprises face a multitude of data integration challenges as they claw their way toward business modernity. Horner explained that this is due to a lack of a metadata standard, causing rampant interoperability between different tools.
“Metadata is the connective thread that weaves disparate data sources, tools, and systems together into a seamless data fabric,” noted Horner.
To further shed light on what a data fabric actually is, Horner cited a definition from Gartner, where a data fabric “utilize[s] active metadata, knowledge graphs, semantics, and machine learning to provide flexible, reusable, and enhanced data integration pipelines, services, and semantics for both operational and analytics use cases across various platforms and processes.”
Clearly, a data fabric architecture is a rather comprehensive, all-encompassing combination of conceptual and technological strategies that invite a more agile business framework. According to many market analysts, “there’s really not a single vendor data fabric solution out there,” explained Horner. “But I beg to differ a little bit. What TimeXtender has been doing since 2006 actually meets the definition of a data fabric.”
TimeXtender offers the Unified Metadata Framework, a holistic, metadata-driven approach toward seamless integration, automation, and a more efficient data integration process. This solution acts as a way to provide a more efficient, agile, and automated approach to data integration, management, orchestration, and governance, solving many of today’s integration challenges.
Ashwin Rajeeva, CTO at Acceldata, thoroughly examined what truly makes a data fabric architecture, echoing Horner’s definition while adding that it offers a “unified and centralized view of data [with a] network-based architecture rather than traditional point-to-point connections.”
Though it sounds ideal, implementation is not without expensive challenges, including data silos, integration complexity, resistance to change, and data trust issues, to name a few. Outside of the scope of data fabric adoption, Rajeeva explained that enterprise data architectures are more complex and varied than ever, adding onto data fabric’s heap of roadblocks.
To resolve these challenges, Rajeeva pointed to data quality as the key enabler of data fabric architectures. As new paradigms like data fabrics will inevitably introduce more complexity, enterprises must begin by evaluating their data foundations; after all, innovation and better business outcomes rely on trustworthy data, noted Rajeeva.
Ultimately, data observability bridges the gap between enterprises and their ambitions of reliable, rapid access to trustworthy data. Data observability covers all six layers of a data fabric, including data access, ingestion, discovery, management, orchestration, and processing, according to Rajeeva.
This sort of widespread automation of data quality “expands across the stack and makes sure that the data that you’re putting into your data fabric is accurate, trustable,” said Rajeeva. On top of that, data observability increases the overall visibility into your data architecture—including into costs and performance—that is necessary for successful data fabric implementation.
For the full, in-depth discussion of data fabric enablers and challenges, you can view an archived version of the webinar here.