What types of data are we analyzing? How much data do we have? What type of database should we use? What platform should we use? What kind of insights are we looking for? How quickly do we need the insights? Does it have to be real-time? These are just a few of the questions that organizations need to address when approaching near real-time analytics. In this blog series, we explore the critical role of real-time analytics and show you how to put it into practice within your organization.
In part one of this blog series, we discussed how to right-size your data processing technique for a variety of business needs. Let’s now dig into the tools and platforms that support streaming analytics.
After knowing your data and the different approaches to data processing, it’s important to focus on the type of database to use. The biggest methodological choice comes down to RDBMS (Relational Database Management System) or NoSQL, and is based on several key factors:
- The type of data (structured or unstructured)
- Volume of data
- Desired type of reporting
Here’s a working example of evaluating these factors in practice:
- Type: The benefit of NoSQL vs RDBMS is that it is seamless, meaning different sources can be stored/recalled easily. OLTP databases are SQL but arguably better at storing rapid writes. NoSQL thrives on ELT (Extract Load Transform) rather than ETL—so when streaming records, you can have them available immediately for querying/recovery and performing transformations down the line for further analysis.
- Volume: If the volume of data is small, it’s best to choose SQL databases. With higher volumes, NoSQL is a better option. NoSQL is highly scalable and performant, which is a stumbling point for a lot of RDBMS.
- Reporting: If you are doing after-the-fact-analysis, it’s better to go with relational databases, whereas if you are looking for near real-time analytics like fraud detection, you should look at an unstructured database like Hadoop.
In support of streaming data processing, new technologies and streaming data are rapidly becoming commonplace in enterprise data architecture. Streaming data requires a data architecture that can handle rapid input and on-time output with efficient data processing. Streaming data architecture has various benefits including handling a constant data stream, real-time and near real-time reporting, detecting compelling patterns in time-series data, etc.
There are various platforms that support streaming analytics, including Apache Flink and Apache Spark. Other cloud-based services are also becoming widely accepted. Data streaming enables analytics teams to extract insights from data with real-time visualization. These platforms and services are helping organizations gain a competitive edge through smarter, better-informed decision-making at the speed of business.
Powered by accelerated insight, near real-time analytics can bring organizations one step closer to competitive differentiation through analytics. But first, harnessing the power of near real-time analytics comes down to a few initial steps including right-sizing your data, data processing technique, supporting database, and platforms.
Click here to read part one in the series.
