Stay Ahead of the Data Curve: Unlock the Power of the Top Big Data Technologies in 2023
Did you know that experts have predicted that by 2025, around 463 exabytes of data will be generated each day (equivalent to 212,765,957 DVDs per day!)? Indeed, the advancing technology and the availability of high computing power have led to the rise of big data and the analytics industry. Big data is massive in volume, and traditional tools cannot be used to process it. As the data generation is in raw format, it is of no use unless it is analyzed and insights are drawn from it. This is the reason big data processing technologies have been introduced in the market that can extract meaningful information from unprecedented amounts of data.
If you are aspiring to become a Big data engineer, then knowing about such technologies is a must. Every company hiring for data-related roles expect professionals to be well-versed in using these technologies. Even when you enroll in any reputed Big data course, its curriculum will involve in-depth coverage of such technologies. In this article, we have mentioned some of the top big data technologies that you can keep track of in 2023.
Artificial Intelligence
Artificial Intelligence is the major driver of the ongoing Fourth Industrial Revolution. Along with its subset machine learning, this technology is finding applications in almost all industries you can think of. When trying to extract hidden trends and patterns from data, machine learning is often used to make predictions. Machine learning models are fed with such massive amounts of data so as to perform effective predictive analytics and gain the necessary outcomes. Organizations are leveraging this technology to enable automation and further applying it in business analytics.
Hadoop
Big data analytics have different phases, one of which is data storage. Now Apache Hadoop is something used in this data storage activity. Basically, Hadoop is an open-source framework that is written in Java and used to handle Big Data and provides storage for structured and unstructured data. Its distinct feature is that it doesn’t store and process data in a single large computer but supports the clustering of several computers to analyze data in parallel rapidly. The major components of the Hadoop ecosystem include Hadoop Distributed File System (HDFS), Yet Another Resource Negotiator (YARN), HBase, and MapReduce.
Apache Spark
Apache Spark falls under the data analytics phase and is referred to as real-time data processing framework. It is a distributed processing system that has been implemented in a variety of use cases to identify patterns and extract real-time insights. In comparison to Hadoop, Spark involves only one step where data is written into memory, operations are performed, and the outcomes are written back, thereby achieving much more rapid execution. Currently, many organizations are using Apache Spark alongside Hadoop for the purpose of processing Big data. Spark on Hadoop generally leverages YARN to share a common cluster and dataset as other Hadoop engines.
MongoDB
MongoDB is another technology that comes under the category of data storage. As mentioned on the official website, MongoDB is a document database (NoSQL) with the scalability and flexibility that users want with the querying and indexing that is required. The database features a document model that is easier for developers to learn and use while still offering the capabilities required to meet the most complex requirements at any scale. Developed in the year 2009, MongoDB has become a NoSQL database of choice when it comes to achieving high availability, horizontal scaling, and geographic distribution. This fully elastic database with end-to-end security is free to use.
Apache Kafka
Apache Kafka is an open-source distributed event streaming platform that is used by more than 80% of all Fortune 100 companies. Organizations that want high-performance data pipelines, data integration, streaming analytics, and to build mission-critical applications prefer using Kafka. Using a cluster of machines with low latencies, Kafka can deliver messages at network-limited throughput. As a user, you are allowed to scale production clusters up to a thousand brokers, petabytes of data, trillions of messages each day, and hundreds of thousands of partitions. Another key feature of the platform is its out-of-the-box Connect interface that integrates with hundreds of event sources and event sinks, including JMS, Postgres, AWS S3, Elasticsearch, and more.
Tableau
You will come across Tableau when you study the data visualization phase of big data analytics. It is basically a visual analytics platform that transforms the way data practitioners use big data to solve many real-world problems. Most organizations prefer using Tableau for their business intelligence initiatives as it is easier to explore and manage data in this platform, allowing faster identification of hidden trends and correlations and sharing of actionable insights with business leaders. This powerful, secure, and flexible end-to-end analytics platform was developed in 2003 and witnessed such immense growth that it was acquired by Salesforce in 2019 (the world’s number one Customer Relationship Management solution). Tableau has helped users deploy and scale a data-driven culture that drives resilience and value through powerful outcomes.
Apart from the ones mentioned above, the big data technology stack also involves SQL-based technologies, Docker, Kubernetes, prescriptive analytics, predictive analytics, data lakes, R programming, and TensorFlow. You can pursue a big data analytics course online to learn more about these interesting technologies.