Depending on how data looks, we can categorize information as structured and unstructured. Learn about the differences to maximize the usefulness of your data.
Depending on how data looks, we can categorize information as structured and unstructured. Learn about the differences to maximize the usefulness of your data.
How do we store data? Read about the definition, devices, and different types of data storage available today.
This article teaches you about Spark's primary data structure: Resilient Distributed Datasets.
Apache Storm is an open-source data streaming technology with low-latency and scalability. In this article, you will learn about the Apache Storm architecture, topology, and use cases.
NewSQL is the middle ground between SQL and NoSQL. Learn about the benefits and features of this database type.
Data integration tools help transport, modify and integrate data into various systems. Learn about the different available tools today and leverage the power of your data.
Elastic Stack generates data that can help you to solve problems and make good business decisions. In consist of open-source software (Elasticsearch, Logstash, Kibana and Beats), each playing an important role in managing and viewing file logs.
Kibana is the user interface of the ELK stack with many querying and visualization features. Learn how to filter through the data using the Kibana Query Language (KQL) and use it to create graphs and dashboards.
Big data servers are servers specifically made for collecting and analyzing unstructured and constantly expanding data from various sources. Learn about the hardware specifications and what software runs on big data servers in this article.
Spark contains three major data structures and APIs for working with big data: RDDs, DataFrames and Datasets. Learn about the difference between them as well as when it's best to apply each.