Big data analytics is the use of advanced analytic techniques against very large, diverse big data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.
What is big data exactly? It can be defined as data sets whose size or type is beyond the ability of traditional relational databases to capture, manage and process the data with low latency. Characteristics of big data include high volume, high velocity and high variety. Sources of data are becoming more complex than those for traditional data because they are being driven by artificial intelligence (AI), mobile devices, social media and the Internet of Things (IoT). For example, the different types of data originate from sensors, devices, video/audio, networks, log files, transactional applications, web and social media — much of it generated in real time and at a very large scale.
With big data analytics, you can ultimately fuel better and faster decision-making, modelling and predicting of future outcomes and enhanced business intelligence. As you build your big data solution, consider open source software such as Apache Hadoop, Apache Spark and the entire Hadoop ecosystem as cost-effective, flexible data processing and storage tools designed to handle the volume of data being generated today.
Businesses can access a large volume of data and analyze a large variety sources of data to gain new insights and take action. Get started small and scale to handle data from historical records and in real-time.
Flexible data processing and storage tools can help organizations save costs in storing and analyzing large anmounts of data. Discover patterns and insights that help you identify do business more efficiently.
Analyzing data from sensors, devices, video, logs, transactional applications, web and social media empowers an organization to be data-driven. Gauge customer needs and potential risks and create new products and services.
Accelerate analytics on a big data platform that unites Cloudera’s Hadoop distribution with an IBM and Cloudera product ecosystem.
Gain low latency, high performance and a single database connection for disparate sources with a hybrid SQL-on-Hadoop engine for advanced data queries.
Use real-time data replication to minimize downtime and keep data consistent across Hadoop distributions, on premises and cloud data storage sites.
Build and train AI and machine learning models, and prepare and analyze big data, all in a flexible hybrid cloud environment.
Learn how they are driving advanced analytics with an enterprise-grade, secure, governed, open source-based data lake.
Hear from IBM and Cloudera experts on how to connect your data lifecycle and accelerate your journey to hybrid cloud and AI.
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