Big data analytics is the often complex process of examining big data to uncover information, such as hidden patterns, correlations, market trends, and customer preferences that can help organizations make informed business decisions.
Data Analytics technologies and techniques give organizations a way to analyze data sets and gather new information. Business intelligence (BI) queries answer basic questions about business operations and performance.
Big data analytics is a form of advanced analytics, which involve complex applications with elements such as predictive models, and statistical algorithms.
Organizations can use big data analytics systems and software to make data-driven decisions that can improve business-related outcomes. The benefits may include more effective marketing, new revenue opportunities, customer personalization, and improved operational efficiency.
Data analysts, data scientists, predictive modelers, statisticians, and other analytics professionals collect, process, clean and analyze growing volumes of structured transaction data as well as other forms of data not used by conventional BI and analytics programs.
Data professionals collect data from a variety of different sources. Often, it is a mix of semistructured and unstructured data. While each organization will use different data streams, some common sources include:
- internet clickstream data;
- webserver logs;
- cloud applications;
- mobile applications;
- social media content;
- text from customer emails and survey responses;
- mobile phone records; and
- machine data is captured by sensors connected to the internet of things (IoT).
Data is prepared and processed. After data is collected and stored in a data warehouse or data lake, data professionals must organize, configure and partition the data properly for analytical queries. Thorough data preparation and processing make for higher performance from analytical queries.
Data is cleansed to improve its quality. Data professionals scrub the data using scripting tools or data quality software. They look for any errors or inconsistencies, such as duplications or formatting mistakes, and organize and tidy up the data.
The collected, processed, and cleaned data is analyzed with analytics software. This includes tools.
Many different types of tools and technologies are used to support big data analytics processes.
Hadoop is an open-source framework for storing and processing big data sets. Hadoop can handle large amounts of structured and unstructured data.
Predictive analytics hardware and software, process large amounts of complex data and use machine learning and statistical algorithms to make predictions about future event outcomes.
Stream analytics tools are used to filter, aggregate, and analyze big data that may be stored in many different formats or platforms.
NoSQL databases are non-relational data management systems that are useful when working with large sets of distributed data. They do not require a fixed schema, which makes them ideal for raw and unstructured data.
A Data Lake is a large storage repository that holds native-format raw data until it is needed. Data lakes use a flat architecture.
A data warehouse is a repository that stores large amounts of data collected by different sources. Data warehouses typically store data using predefined schemas.
Knowledge discovery/big data mining tools, enable businesses to mine large amounts of structured and unstructured big data.
Data integration software, enables big data to be streamlined across different platforms, including Apache, Hadoop, MongoDB, and Amazon EMR.
Spark, is an open-source cluster computing framework used for batch and stream data processing.
Big data analytics applications often include data from both internal systems and external sources, such as weather data or demographic data on consumers compiled by third-party information services providers. In addition, streaming analytics applications are becoming common in big data environments as users look to perform real-time analytics on data fed into Hadoop systems through stream processing engines, such as Spark, Flink, and Storm.