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SQL Server Insider: Tips for the SQL Server Pro Vol. 8

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Top Cloud Data Warehouses for the Enterprise: Amazon vs. Azure vs. Google vs. Snowflake

Modern cloud architectures combine three essentials: the power of data warehousing, flexibility of Big Data platforms, and elasticity of cloud at a fraction of the cost to traditional solution users.

But which solution is the right one for you and your business?

Download the eBook to see a side-by-side comparison of the leading cloud data warehouse vendors and explore:

- The top cloud data warehouses at a glance 
- Each vendor's noteworthy differences and proven approach to make them accessible, effective, and efficient for all your data users.
- How the Qlik® Data Integration Platform can help you achieve an agile data warehouse and accelerate analytics initiatives.

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  • Knowledge graph real-world performance testing: Results inside

    Enterprises often trip over their own data when the business landscape shifts, or new information needs arise.

    And although conventional relational database management systems were able to manage when the data landscape was predominantly structured, now that the landscape is voluminous, varied, and changing, enterprise data fabrics are providing a new path forward.

    Read this e-book to learn about Stardog’s enterprise-class Knowledge Graph, which takes advantages of these data fabrics, and that consists of materialized and virtualized graphs that span multiple cloud platforms.

  • Jargon buster guide to database management

    Database management roles have long been a rich career seam for IT professionals. However, there has been, in recent years, a “Cambrian explosion” in the previously steady but sleepy world of databases.

    The relational database model, breaking the traditional dependency between data storage and applications, held sway in enterprise IT for decades, from the time of its serious commercialisation by Oracle in the mid-7os. Also hegemonic has been the language implementation of the relational model, SQL – Structured Query Language; which is of the same mid-70s vintage.

    But, in recent years, with the rise of the big data technologies of the Hadoop stack and the NoSQL databases, the data store and database scene has become more complex.

    And when it comes to the Oracle database, its creator announced it to be “autonomous” at Oracle Open World in San Francisco October 2017. What does this mean for database administrators (DBAs)? Does it mean the opening up of new roles of higher intellectual interest and value? Or does it mean redundancy and early retirement?

    As always with IT, the technology is only one-third of the story – the others being process and, crucially, people. What are the operational and organisational design problems to be solved in this more heterogenous database management landscape?

    This Jargon Buster e-guide to contemporary database management is an aid to stepping back and thinking afresh about an area of enterprise IT that has many decades of heritage. Or, as many sometimes (arguably rudely) put it, legacy.

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