For years, data has been generated in many ways, and it continues to grow in various forms. Today, this trend is in a transformative stage. The data generation rate is high, and the type of data being generated in almost every field surpasses the capability of existing data storage techniques.
As reported by Network World, International Data Corporation (IDC) expects that worldwide data will grow to 175 zettabytes within the next 5 years, and nearly 30 percent of that data will be consumed in real time.
Many tools and techniques are available for managing and analyzing data. However, traditional technologies have limited ability to manage big data and derive useful insights. Thus, to meet those needs, new technologies have been developed.
With SQL Server 2019, you can create a secure, hybrid, machine learning architecture with capabilities ranging from preparing data and training a machine learning model to operationalizing that model and using it for scoring. SQL Server 2019 Big Data Cluster makes it easy to unite high-value relational data with high-volume big data.
The challenge of big data analytics is to explore traditional techniques—such as rule-based systems, pattern mining, decision trees, and other means of data mining—to efficiently develop business rules on large datasets. Big Data Cluster brings together multiple instances of SQL Server with Spark and the Hadoop Distributed File System (HDFS). With this capability, you can more easily unite relational data and big data, and use the data in reports, predictive models, applications, and artificial intelligence (AI).
MongoDB is another prominent, open-source tool that is cross-platform-compatible with many programming languages and supports multiple operating systems. It is an object-oriented NoSQL database that is used for high-volume data storage. The key features of this tool are its support for storing any type of data and partitioning data across multiple nodes and data centers. MongoDB supports cloud-native deployment and offers great flexibility of configuration.