What’s Ahead for Big Data? Tableau Makes 10 Predictions

It’s been a little more than 10 years since Yahoo deployed a Hadoop cluster designed for storing large amounts of unstructured information and ushered in an era of Big Data. Now, what’s next?

Tableau has made 10 predictions for what you can expect from Big Data this year.

1. Greater Need for Speed

The desire for more speed exists with just about any technology, from computer databases to kitchen blenders. We tend to think: OK, we built it and we can use it. Now we want it to work faster. That’s what’s happening in the world of big data. In fact, the speed capability of queries and analyses often comes up as the first concern of users.

The demand for speed – already evidenced by the adoption of faster databases like Exasol and MemSQL – will continue. As the saying goes, it isn’t the big that will eat the little; it’s the fast that will eat the slow.

2. Obsolete Hadoop-Dedicated Tools

Users have come to realize that solutions to their problems come from a variety of sources, such as cloud warehouses and structured and unstructured databases. Hadoop as a sole data source just won’t do anymore.

The first sign of this trend came with the exit of Platfora, which had essentially tied itself to Hadoop for data analysis, data visualization and sharing.

The result: Platforms that are purposely built for Hadoop will wither. Platforms that are source- and data-agnostic will thrive.

3. Quicker Access and Use of Data

The emphasis used to be on collecting and storing data, in so-called “data lakes.” However, the new emphasis is on near-immediate use of data as it’s collected and stored.

4. Case-Specific Architecture for Hybrid Needs

Expect architectures to provide the best self-service data-prep tools, Hadoop Core and end-user analytics platforms in ways that can be reconfigured as needs evolve.

The trend started when users came to expect Hadoop to be more than just a batch-processing system, and the trend will continue this year.

5. Variety Driving Big-Data Investments

Of the “three V’s” that make up big data – velocity, volume, variety – expect variety to get the most attention and funding.

Data formats and multiplying are becoming more and more crucial, along with connectors. Because of that, the focus will be on connectors of disparate sources.

6. Rise of Apache Spark

Once just a component of Hadoop, Spark has become the favorite of data architects, IT managers and BI specialists. The reasons: Spark isn’t batch-oriented and lends itself to interactive applications and real-time stream processing.

Also on the rise: Microsoft Azure ML, because it’s beginner friendly and easily integrates with other Microsoft tools.

7. Demand for Tools to Connect Cloud-Hosted Data Services

The amount of data stored on the cloud will increase. Tools that can combine data from multiple services will be invaluable in many areas.

Example: The Internet of Things (IoT) is generating massive volumes of structured and unstructured data, and more of the data is being deployed through cloud services. So robust tools are needed to draw links between this data.

The result will be more and better opportunities for self-service analytics.

8. Self-Service Data Prep for Users

Business users want accessibility to data, and they also want to reduce the time and complexity required for data analysis. Innovation will focus on those requirements.

The innovators in this field: Alteryx, Trifacta and Paxata.

9. Additional Enterprise Standards

Apache Sentry, Apache Atlas, and Apache Ranger will take bigger roles in the data governance and security components surrounding enterprise systems.

One big driver of this development: Customers are starting to expect it.

10. Increased Use of Metadata Catalogs

Data sometimes gets lost or discarded because there’s just too much of it, so no one’s sure what data is worth analyzing. Expect that to change. Machine learning will automate the task of finding and using relevant data in Hadoop.

Most market analysts tag this as a can’t-miss development, and with good reason: It helps data consumers and data managers reduce the time needed to trust, find and accurately query the data.

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