Key Trends and Predictions for Big Data in 2016
Leading Data Trends for 2016
In 2016, the world of big data witnessed a multitude of significant trends and predictions.
1. Increased Adoption of Big Data Technologies
The adoption of key big data technologies such as Hadoop and Spark saw a substantial increase, with these technologies becoming more mainstream in businesses for analysing and processing large volumes of data. Additionally, the concept of data lakes, which store raw, unprocessed data, gained traction, allowing companies to store data in its native format for future analysis.
2. Integration with IoT and Other Technologies
Big data began to integrate more with the Internet of Things (IoT), enabling businesses to analyse data from sensors and devices, leading to more efficient operations and decision-making. Furthermore, cloud computing was becoming more integrated with big data analytics, allowing for scalable solutions and easier data management.
3. Privacy and Security Concerns
With the increasing collection and analysis of big data, privacy concerns were on the rise. There was a need for better privacy protections and policies to safeguard user data. Ensuring the security of big data was also becoming a priority, as unauthorized access to large datasets could have significant consequences.
4. Growing Importance of Real-Time Analytics
The ability to process and analyse data in real-time, known as "fast data," was becoming more important for businesses. This allowed for quicker decision-making and response to market changes.
5. Emerging Role of Artificial Intelligence (AI)
Early stages of AI and machine learning were beginning to be integrated into big data analytics. This was seen as a future trend that would enhance data processing capabilities.
Other Developments
Self-service data preparation tools are becoming popular, reducing the time and complexity of preparing data for analysis in the big data environment. In the upcoming year, NoSQL technologies are expected to become a leading piece of the enterprise IT landscape, with companies like MongoDB, DataStax, Redis Labs, and MarkLogic outnumbering traditional database vendors in Gartner's Leaders quadrant.
The spread of self-service data analytics, along with widespread adoption of the cloud and Hadoop, are leading to many changes in the industry. Apache Spark has become the big data platform of choice for many enterprises, offering significantly increased data processing speed compared to Hadoop. Analysts predict that 90% of companies who have adopted Hadoop will keep their data warehouses, and cloud offerings allow customers to dynamically scale storage and compute resources in the data warehouse relative to the larger amounts of information stored in their Hadoop data lake.
The job of a data analyst is becoming more exciting due to the changes and new ways of working with big data. A survey of 2,200 Hadoop customers shows that 76% of those who already use Hadoop plan on doing more with it within the next three months, while only 3% anticipate doing less. Hadoop is becoming a core part of the enterprise IT landscape, with investment growing in components surrounding enterprise systems such as security, as demonstrated by the Apache Sentry project.
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