Introduction Microsoft announced in September 2018 that SQL Server 2019, which is now in preview, will have a Big Data Cluster deployment option. This is a Big-Data-capable SQL Server with elastic scale and extended Artificial Intelligence (AI) capabilities, mostly as a result of deep integration of Hadoop and Spark out-of-the-box. The new SQL Server Big […]
2019-09-09
3,424 reads
When it comes to data warehouse modernization, we’re big fans of moving to the cloud. ...
2019-04-17
The overall importance of data and information within organizations has continued to grow. We’ve also seen the continued rise of megatrends like IoT, big data – even too much...
2019-04-16
For many companies, the initial attraction to Azure Databricks is the platform’s ability to process big data in a fast, secure, and collaborative environment. However, another highly advantageous feature is the Databricks dashboard.
2019-03-30
This post describes how to generate big datasets with Hive in HDInsight, specifically TPC-DS benchmarking datasets. There are many tools for generating sample data, and this one is particularly nice due to its familiarity and ability to generate massive...
2019-03-30
Whether you are running an RDBMS, or a Big Data system, it is important to consider your data-partitioning strategy. As the volume of data grows, so it becomes increasingly important to match the way you partition your data to the way it is queried, to allow 'pruning' optimisation. When you have huge imports of data to consider, it can get complicated. Bartosz explains how to get things right; not perfect but wisely.
2016-11-22
3,345 reads
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
By Vinay Thakur
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