Continuing the knowledge on “What is Big Data?“, this article will reveal some of the top technologies for big data that businesses can use. The amount of data created, collected and used globally is forecast to reach 149 zettabytes by 2024. For businesses to store, process and analyze this data, Big Data Technology is required.
I. What is Big Data Technology?
Big Data technologies are software utilities designed to analyze, process, and extract information from large data sets. Usually, this data has a large volume and a very complex structure that cannot be handled by traditional technologies.
III. Classification of big data technology
This type of big data technology includes the infrastructure that enables data to be fetched, stored, and managed. Various software programs can easily access, use and process data quickly.
Data mining is the process of extracting useful information from raw data and analyzing it. Typically, this data is high in volume with high variability and streams at lightning speed. So, data extraction is almost impossible without special technology.
In data analytics, technologies are used to clean and transform data into valuable information to support business decision-making. Big data analytics tools can provide insights into customer preferences and market trends.
Data visualization technologies use visual elements such as graphs, charts, and maps to explain trends, patterns, and outliers in data. The data is processed to create graphical illustrations that allow people to capture large amounts of information in seconds.
III. Top Big Data technologies
1. Apache Hadoop
Apache belongs to the category of Data Storage technology.
It is an open source software platform for storing and processing big data using the MapReduce programming model. This technology is flexible and scalable to handle all data formats and can recover from failure. Apache Hadoop is the most commonly used big data engine.
Presto is a representative of the Data Mining technology category.
Developed by Facebook, Presto is an open source SQL query engine that allows query analysis of huge amounts of data. This technology can query where data exists without moving data into separate analytics systems. In particular, a single query on Presto can combine data from multiple sources within an organization and perform analysis in minutes.
3. Apache Spark
Apache Spark is a prominent name in Data Analytics technology.
This is a popular Big Data technology for data analysis because it is fast and efficient when running applications. Spark has built-in features for SQL, machine learning, graph processing, and stream analysis. In addition, it can be easily integrated with Hadoop to perform quick tasks depending on the business needs of the enterprise.
Besides Apache Spark, Kafka is also a Big Data technology of the Data Analytics category.
Kafka is a system for collecting, storing, reading, and analyzing streaming data at scale. For real-time online data analysis, Kafka can be seamlessly integrated with Apache Spark. This platform is used by thousands of organizations, including Twitter, Spotify, Netflix, Linkedin. The only downside of Kafka is the lack of good monitoring solutions.
Tableau belongs to Data Visualization technology.
It makes it easy for users to create various types of charts and dashboards for data visualization and analysis. With Tableau, users can work on live data sets to gain valuable information and improve decision making. Thereby helping to promote business development.
Finally, Big Data is still evolving with many applications of existing big data technologies. Besides the popular technologies mentioned above, AI, NoSQL or Blockchain are also leading names. The implementation of Big Data Technology in business brings a lot of benefits. In order to get the most out of the technologies available in the market, businesses need to identify the type of problem they are facing. This will help choose the best solution.