It is worth getting familiar with Apache Spark because it a fast and general engine for large-scale data processing and you can use you existing SQL skills to get going with analysis of the type and volume of semi-structured data that would be awkward for a relational database. With an IDE such as Databricks you can very quickly get hands-on experience with an interesting technology.
2016-11-18
3,131 reads
What is next for big data? Some experts claim that data "volumes, velocity, variety and veracity" will only increase over time, requiring more data storage, faster machines and more sophisticated analysis tools. However, this is short-sighted, and does not take into account how data degrades over time. Analysis of historical data will always be with us, but generation of the most useful analyses will be done with data we already have. To adapt, most organizations must grow and mature their analytical environments. Lockwood Lyon shares the steps they must take to prepare for the transition.
2016-06-03
10,764 reads
The next few years will be critical for the information technology staff, as they attempt to integrate and manage multiple, diverse hardware and software platforms. In this article, Lockwood Lyon addresses how to meet this need, as users demand greater ability to analyze ever-growing mountains of data, and IT attempts to keep costs down.
2016-05-09
5,553 reads
Integrating big data appliance solutions into a data warehouse requires preparation and forethought. DBAs and business data consumers must work together both to address the implementation issues above and to meet the needs of multiple business data consumers. Lockwood Lyon discusses the topic.
2015-12-11
4,993 reads
What are the most popular SQL implementations for Hadoop? How different are they from T-SQL?
2015-11-24
5,057 reads
Learn where to get the latest installation and learning resources for the ever-evolving components of Hadoop ecosystem and how those components may complement Microsoft SQL Server common everyday tasks.
2018-05-04 (first published: 2015-10-01)
4,326 reads
As big data application success stories (and failures) have appeared in the news and technical publications, several myths have emerged about big data. This article explores a few of the more significant myths, and how they may negatively affect your own big data implementation.
2015-03-25
11,670 reads
Most large organizations have implemented one or more big data applications. As more data accumulates internal users and analysts execute more reports and forecasts, which leads to additional queries and analysis, and more reporting. The cycle continues: data growth leads to better analysis, which generates more reporting. Eventually the big data application swells with so much data and querying that performance suffers.
2015-02-26
11,157 reads
Big data applications are now fairly commonplace in large organizations. It is, however, difficult to simply ‘drop’ these applications into an existing IT infrastructure and expect to run smoothly. In addition to energy and cooling requirements for new hardware to support the new big data application, other IT areas need to prepare.
2014-10-27
9,144 reads
Big Data implementations are more than just lots of data. Of equal importance is the analytics software used to query the data. Analyzing business data using advanced analytics is common, especially in companies that already have an enterprise data warehouse. It is therefore only natural that your big data application must be integrated with the existing warehouse.
2014-09-26
10,283 reads
Hello Hello, We. Are. Back! The schedule for EightKB 2024 Edition has been announced!...
By Steve Jones
I had a customer that was looking to document a restore that had occurred...
By DataOnWheels
DIVERSIFY! We devote a lot of time to mastering the technology that we are...
Comments posted to this topic are about the item 7 sept, scheduled book
Comments posted to this topic are about the item 7 sept, schedlued article
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