Twice a year we look at the top content over the last year to do a deeper dive into what people are reading. Over the years, we have seen a definite shift in interest. Five years ago, Big Data had hit mainstream media and people were doing searches to educate themselves on NoSQL (Not only SQL) databases being made available to store and access both structured and unstructured data. The three V’s of Big Data (then four and even five Vs) were the topic of the day. What should be stored in the Cloud was another hot topic.
Since then we have seen a shift from people wanting to know the difference between a key-value and graph database to wanting to know how to incorporate these new databases into their legacy Data Models. And that’s still a hot topic today, the how’s of Modeling NoSQL.
And now we’re seeing the pendulum of interest swinging from the storage to the analytics. Businesses have gone from being completely overwhelmed with the onslaught of data, trying to figure out how to just capture everything relevant to the business and store it, to now having better insight on what they need to know and how to use the data to drive better business decisions. How to access the data captured and put it to use are the current key drivers today.
To build a Data Lake or to not build a Data Lake is a very hot question of the day. While this is a data storage topic, the accessibility of the data for analysis drives the question. We have seen an incredible amount of interest on Data Lakes vs Data Warehouse.
Metadata has resurfaced as one of the hottest topics over the last year. “How does it handle the metadata?” or “How do you handle the metadata in this scenario?” are two of the most common questions we get now in our webinars, no matter what the topic.
On the analytics side, businesses are looking at the difference between descriptive, predictive, and prescriptive analytics and how to use all three effectively. Data Science and Machine Learning have become two of the hottest keywords. There is a huge emphasis on the ability to access real-time analytics with less time running reports and more time spent on implementing decisions made because of the analysis.
And of course, Data Governance and Quality rules it all. With the ever-shifting Data Architecture to incorporate these new technologies, how to govern the data and maintain quality is an ever-present interest no matter the level of data with which people are working.
And on that note, let me introduce to you the Top 20 posts published by DATAVERSITY over the last year:
- Article: 2017 Machine Learning Trends
- Article: A Brief History of the Internet of Things
- Article: A Comprehensive Review of Skills Required for Data Scientist Jobs
- Article: Data Science Predictions for 2017
- Article: A Brief History of Deep Learning
- Article: A Review of Different Database Types: Relational versus Non-Relational
- Article: 2017 Trends for Semantic Web and Semantic Technologies
- Article: Data Lakes 101: An Overview
- Article: 2017 Predictions for Data Quality and Data Governance
- Article: Business Glossary Basics
- Blog: Data Governance vs. Big Data Governance
- Article: Artificial Intelligence Use Cases: An Overview
- Blog: Debugging Complex SQL Queries
- Article: A Brief History of Database Management
- Article: Data Lineage and Data Quality: Two Vital Elements for Enterprise Success
- Article: The Data Warehouse: From the Past to the Present
- Article: Microservices for Big Data: Flipping the Paradigm
- Article: Self-Service Business Intelligence is Big, but is it for Everyone?
- Article: Enterprise Data World 2017: Data Driven Business Transformation
- Article: The Future of Data Management: An Evolution of the Industry