Watch this video to learn about the real market dynamics -- rather than the hype -- that are driving big data adoption.
Dresner Advisory Services started covering big data analytics two years ago. For its 2017 study, it surveyed 200 respondents from around the world. Respondents included IT and line-of-business executives. Among the respondents, the general level of knowledge about big data has risen as much of the hype around big data subsided. Mainstream organizations are adopting big data in greater numbers. And organizations that weren’t considering adoption of big data a couple of years ago are now.
This video reveals the numbers behind the trends in big data adoption.
Forty-one percent of survey respondents reported using big data, which was double the number from 2015. And fewer companies were reporting no plans to use it. Early adopters are finding practical and achievable projects for their big data ambitions. For example, 45 percent of surveyed financial services firms reported use. Technology companies were also active early adopters. Even for late adopters like education, maturing big data technologies have increased the intention to adopt. And this trend holds across companies of all sizes.
In this video, you’ll learn which use cases attract the most big data attention.
Data warehouse optimization was the leading use case followed closely by customer analysis. IoT uses cases are not on the radar for many organizations right now, but it’s likely that they will predominate big data analytics in the relatively near future. Watch the next video to learn what’s driving choices in big data infrastructure.
Watch this video to see who the major players are in big data infrastructure and why.
Of course, Spark and MapR are battling it out for the top spot when it comes to Hadoop. Both have increased their footprint, but Spark is the clear leader. Spark’s powerful, in-memory data processing engine is gaining in popularity. When it comes to big data analytics, speed is king. And among the early adopters like technology, finance, and healthcare it’s the clear choice. Yet MapReduce is holding it’s own thanks to its utility with legacy applications.
Check out this video for insight into big data access methods.
Spark SQL, Hive, and Hive QL offer different approaches to accessing data stored in Hadoop. The Hive alternatives are preferred by those accustomed to typical query languages. Amazon Redshift and Google BigQuery also have adherents in this corner of the big data space. Organization’s show data access preferences based on size and industry. Tech and financial services often adopt Spark SQL, but size plays into this dynamic. Smaller organizations tend to lean more towards Spark SQL, while larger organizations lean towards Hive and Hive QL even though they also show use of Spark SQL.
This video looks at the role of search in big data.
As text analytics and natural language processing gain importance in business intelligence efforts, big data search will gain more of the spotlight. Elasticsearch, Apache Solr, and Cloudera Search are the main players right now. Cloudera Search is Apache Solr implemented in the Hadoop environment. Sentiment analysis of social media data from sources like Facebook and Twitter is one use case where big data search will play an important role.
Watch this video for information about the importance of big data distributions.
There are four dominant Hadoop distributions: Cloudera, Hortonworks, Amazon, and MapR. All four are gaining increased interest as the level of big data adoption grows. The market leader right now is Cloudera although high tech prefers Amazon as do smaller organizations -- up to 1,000 employees.
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