Top Trends in Modern Data Architecture for 2019

From AI and machine learning to data discovery and real-time analytics, a strong data architecture strategy is critical to supporting your organization's data-driven goals. Greater speed, flexibility, and scalability are common wish-list items, alongside smarter data governance and security capabilities. Many new technologies and approaches have come to the forefront of data architecture discussions, including data lakes, in-memory databases and engines like Spark and cloud services of all shapes and sizes.
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Spotlight

OTHER ON-DEMAND WEBINARS

Organizing the Chaos of Data: The Importance of Database Microservices in the Cloud

As companies invested in their digital transformations, one side effect was the proliferation of data environments and varying database instances. With so many systems to navigate, many organizations have fallen prey to operational disruptions and now struggle to achieve the high level of performance and scalability that their transformations promised.
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Metadata Governance for Catalogs, Glossaries, Dictionaries, and Data

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Metadata Governance is the execution and enforcement of authority over the management of Metadata and other data documentation. Organizations that govern their data documentation find it easier to govern their data as a result. There is direct correlation between the use of Data Catalogs, Business Glossaries and Data Dictionaries and successful governance of data and Metadata. This month’s RWDG webinar with Bob Seiner will focus on governing the use of the mentioned tools and the Metadata that can be managed inside each one. Bob will talk about governing Metadata in existing Metadata resources versus using new tools to handle this function.
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How does AXA Belgium Replicate its operational data into its Data Lake?

To preview your data, you must first have access to it. This operation can be difficult when distributed into different systems and silos which is why many companies choose to bring them together in a Data Lake. However, without governance or stewardship, these data lakes are rapidly turning into swamps, veritable brakes on innovation.
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DataOps and Cloud Data Ecosystems: Overcoming the 3 Biggest Hurdles

The cloud is changing the way we use data and at the same time, the way we use data is changing the cloud. Organizations are adopting multiple cloud data platforms to maximize data’s value, but in doing so are often increasing complexity for data engineering and DataOps teams that may stifle the very reason for investing in those platforms.
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