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10 big data trends we are seeing in 2017

By Paul Lyons - Wednesday, July 5th, 2017

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With more and more organisations storing, processing, and extracting value from data, the systems that support such information brings has undoubtedly risen in 2017.

 

What’s more, there will be demand for platforms that give custodians greater security while empowering end users to analyse their data.

 

Here are the top ten big data trends we are seeing in 2017:

 

1. Variety over volume and velocity

 

Variety is quickly becoming the single biggest driver of big-data investments. Analytics platforms will also be evaluated on their ability to provide direct connectivity to disparate sources.

 

2. The rejection of one-size-fits-all frameworks

 

Expect to see organisations pursuing use case-specific architecture design based on factors including user personas, questions, volumes, frequency of access, speed of data, and level of aggregation.

 

3. Faster, more approachable data

 

2017 has seen an increasing adoption of databases and technologies that enable faster queries. Business users want to use Hadoop data for faster, more repeatable KPI dashboards along with exploratory analysis.

 

4. The fall of Hadoop-specific tools

 

Previously, several technologies rose with the big-data wave to full the need for Hadoop-based analytics. But this year, platforms that are data- and source-agnostic will thrive instead.

 

5. The rise of machine learning

 

Thanks to big-compute-on-big-data capabilities, platforms featuring computation-intensive machine learning, AI, and graph algorithms are on the up in 2017.

 

6. Leveraging data lakes

 

Businesses are now demanding repeatable and agile use of the lake for quicker answers. However, they will need to think carefully about business outcomes before investing in personnel, data, and infrastructure.

 

7. New self-service analytics opportunities

 

As a result of IoT connected devices, where accessing and understanding data on cloud services is often difficult, demand has grown for analytical tools that seamlessly connect to and combine a wide variety of sources.

 

8. Self-service data prep

 

Although making Hadoop data accessible to business users is a big challenge, self-service prep tools could be the answer. They allow Hadoop data to be prepped at the source and make data available as snapshots for faster, easier exploration.

 

9. Analysis-worthy data from metadata catalogs

 

To overcome the issue of sifting through too much unorganised information, metadata catalogs can help users discover and understand relevant data worth analysing using self-service tools.

 

10. Hadoop adds to enterprise standards

 

In 2017, we are seeing more investments in the security and governance components surrounding enterprise systems, where Hadoop is sure to play a key part.

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