Big data platforms flourish, which has upsides and drawbacks for imminent clients. Hadoop clusters, the Spark processing engine, NoSQL databases, even conventional databases and data warehouses these and a variety of other technologies can all be tapped to create a big data architecture. But it’s possible to go down the wrong technology path — or multiple wrong paths.
It’s up to IT administrators, enterprise architects and others involved in building a big data framework to keep their organization on track to meet the business goals behind the deployment.
“You need to make sure your architecture will take you where you want to go,” said Ibrahim Itani, an independent consultant who focuses on big data analytics and a former leader of analytics and data warehousing teams at Verizon.
Amid a board discourse at the 2017 TDWI Leadership Summit in Las Vegas, Itani contrasted architecting huge information conditions with planning spans with numerous paths and levels that can deal with various movement needs.
In the two cases, he stated, you need to envision future use so you can reconfigure or develop best of similar establishments. Altering a big data architecture “is exorbitant and ruinous to business operations if significant changes are required frequently,” Itani forewarned.
He included, however, thatb ought to have the capacity of big data systems oblige new platforms and tools as they develop or as business needs change.
Edd Wilder-James, an expert at Silicon Valley Data Science, also pointed to technology agility as a key element of well-designed big data architectures.
In addition, he cited related attributes such as predictive analysis and data visualization, plus support for schema-on-read approaches to data modeling, which provide flexibility in how information is organized.
“Not all data is equal,” Wilder-James said, in a session at the TDWI conference. “We need to treat different data in different ways. The things we need to consider are substantially more confused than some time recently.
To help address such difficulties, many organizations are deploying multiple big data platforms to handle different parts of the processing pipeline.
This guide includes a wide To help address such difficulties, range of content on the available platform options, including Hadoop, Spark and database technologies.
In the sections below, you’ll find guidance on navigating the technology selection process, real-world examples of big data programs and information on big data management trends and technology developments.
Understanding Of Choosing The Right Big Data Platforms
Hadoop once seemed to be synonymous with big data, and it’s still a key part of most big data architectures. But the big data technology landscape has broadened to include other platforms that are augmenting Hadoop in user deployments — or, in some cases, replacing it altogether.
The increased menu of technology choices gives organizations more flexibility for meeting their application needs; it also expands on Hadoop’s original batch processing focus to enable stream processing and real-time analytics.
The articles in this section highlight various big data platforms and provide advice on what they’re suited for and how to use them effectively.
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