Access your Pro+ Content below.
Accelerate Apache Spark to boost big data platforms
This article is part of the Modern Infrastructure issue of April 2017, Vol. 6, No. 4
So, we have data -- lots and lots of data. We have blocks, files and objects in storage. We have tables, key values and graphs in databases. And increasingly, we have media, machine data and event streams flowing in. It must be a fun time to be an enterprise data architect, figuring out how to best take advantage of all this potential intelligence -- without missing or dropping a single byte. Big data platforms such as Spark help process this data quickly and converge traditional transactional data center applications with advanced analytics. If you haven't yet seen Spark show up in the production side of your data center, you will soon. Organizations that don't, or can't, adopt big data platforms to add intelligence to their daily business processes are soon going to find themselves way behind their competition. Spark, with its distributed in-memory processing architecture -- and native libraries providing both expert machine learning and SQL-like data structures -- was expressly designed for performance with large data sets. ...
Access this PRO+ Content for Free!
By submitting your personal information, you agree that TechTarget and its partners may contact you regarding relevant content, products and special offers.
Features in this issue
IT shops with specific workload requirements continue to choose specialty IaaS offerings over the more rigid services from cloud giants like AWS, Azure and Google.
The evolution of container management platforms may be generating a lot of buzz, but the software needs further improvement before it makes sense for organizations to invest in it.
With big data architectures and cloud resources finally making machine learning applications possible, Monte Zweben says it's time to map out your AI-enabled futures.
More data center teams have moved to software-defined networks. And while the technology brings new benefits around automation, it also brings a host of implementation challenges.
Columns in this issue
Big data platforms like Apache Spark process massive volumes of data faster than other options. As data volumes grow, enterprises seek ways to speed up Spark.
The misuse of the phrase 'cloud computing' has created misperceptions of the technology. Remember that the cloud isn't a location -- it's a more agile way to design IT services.