In this data-driven economy, the key differentiator between a thriving business and a mediocre one is how it approaches data management. Big data has been a buzzword in recent years, and in the near future, it just might be the biggest disruptor in business as it gradually seeps into its every aspect—from simple online transactions to complex queries that lead to real-time, actionable insights.
Due to the constant growth of big data, both in breadth and complexity, businesses have turned to in-memory computing to help make sense of it all. Solutions like in-memory data grids allow companies to boost business results with event-driven analytics, which trigger a method whenever a specified event occurs. This provides real-time notifications for business-critical transactions and gives businesses ample time to address issues before they become major concerns. Speed is the main draw of in-memory solutions and, arguably, the reason why most organizations that handle big data turn to them. In-memory computing can boost performance of data processing systems and make them more than 100 times faster than disk-based solutions.
There are a number of big data technologies that aim to make data management less complex and quicker for businesses. These technologies fall under four main categories:
- Data analytics
- Data mining
- Data visualization
- Data storage
Top Data Analytics Technologies
Apache Spark is an open-source framework that comes with built-in modules for machine learning, streaming, graph processing, and support for SQL, making it a versatile solution for data processing. It also supports the important big data languages, including Java, Python, and Scala. Spark helps decrease waiting times between queries and the time it takes to run applications. With the capability to access diverse data sources, Spark can run on any platform, including Hadoop, Kubernetes, Apache Mesos, or using its standalone cluster.
Apache Kafka is an open-source event streaming platform with a distributed structure. As such, it stores simple byte arrays or messages as topics so that they can be partitioned and replicated across different nodes. It can connect to almost any event source and event sinks out of the box, including JMS, AWS S3, and Elasticsearch.
Top Data Mining Technologies
Presto is an open-source distributed SQL engine designed for querying data where it resides. Presto queries allow for analytics across an entire organization through the combination of data from multiple sources. Presto can run interactive analytic queries against data sources of any size.
Elasticsearch is a distributed search and analytics engine based on the Apache Lucerne library. Featuring a full-text search engine that’s multitenant-capable, it comes in an HTTP interface and with schema-free JSON documents. Data can be sent to Elasticsearch in the form of JSON documents, and these are automatically stored with the addition of a searchable reference to the document in a computer cluster’s index. The Elasticsearch API can then be used to search and retrieve the JSON document quickly and easily.
Top Data Visualization Technologies
A common choice for business intelligence (BI), Tableau allows you to create a range of visualizations that present data interactively and make getting actionable insights easier. What makes Tableau a popular choice is its real-time data analytics capabilities that also come with a tool that allows you to drill down data and view its impact in a visual format. This makes it accessible to anyone, regardless of IT experience.
Plotly develops online visualization and data analytics tools that provide statistics and online graphing tools. The platform makes it easy for individuals and teams because it provides tools that are both collaborative and inclusive. It also provides scientific graphing libraries for Python, Perl, Arduino, REST, Julia, R, and MATLAB.
Top Data Storage Technologies
The Hadoop Framework is designed primarily for the storage and processing of data in a distributed data processing environment that can be run on commodity servers using a simple programming model. It is optimized for horizontal scaling from a single machine to thousands of servers, with each providing local computation and storage. The library is designed to detect and handle failures at the application layer to ensure availability of service.
MongoDB offers flexibility in handling a variety of data types in large volumes and across distributed architectures. This NoSQL database does away with the rigid schema of relational databases, using JSON-like documents with optional schemas. It supports common features like ad-hoc queries, replication, file storage, indexing, transactions, and more.
Big Data, Big Evolution
As big data becomes more a part of critical business processes, the technologies that come with it evolve and become more complex. The tools mentioned above can be used to ensure that businesses are not compromised and that they keep their competitive edge over competitors. Data scientists and engineers need the tools that can help them pull clean data and determine patterns that point to actionable insights that businesses can act upon. Ideally, these patterns can be used as models in building predictive analytics platforms in the long term.