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8 Big Data Technologies On the Rise

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Big Data Technologies

Big Data Technologies

Big Data Technologies-The media storm surrounding big data has calmed, but businesses are still searching for ways to harness all this data.

As a matter of fact, research firm IDC forecasts that global spending on big data technologies will reach $260 billion by 2022 – with a hard focus on big data analytics.

Industries like manufacturing, banking, professional services, entertainment, and even the federal government are going all-in on big data. So, what other technologies are on the rise?

8 big data technologies

See something you like? Feel free to skip ahead to read more about it:

  1. Hadoop
  2. Big data programming languages
  3. NoSQL databases
  4. Data lakes
  5. Advanced analytics
  6. Stream analytics
  7. Edge computing
  8. Self-service options

1. Hadoop

Hadoop has been around for quite some time, but it’d be difficult to compile a list of big data technologies without mentioning it.

The Hadoop ecosystem is an open-source framework with many products dedicated to storing and analyzing big data. For example, some of the more popular products include MapReduce for big data processing, Spark for in-memory data flow, Hive for analytics, and Storm for distributed real-time streaming.

Hadoop adoption is still on the rise. An estimated 100 percent of enterprises will likely adopt Hadoop-related technologies for analyzing big data.

See what real users are saying about Hadoop and its suite of products.

2. Big data programming languages

You also can’t mention Hadoop without mentioning the lineup of big data programming languages used for large-scale analytical tasks as well as operationalizing big data. Here are the four languages below:

Python – With more than 5 million users, Python is easily the trendiest programming language right now. Python is particularly useful with machine learning and data analysis, not to mention it has coherent syntax – making it more approachable for beginner coders.

– This open-source language is widely used for big data visualization and statistical analysis. The learning curve for R is much steeper than Python, and it’s more used by data miners and scientists for deeper analytical tasks.

Java – It’s worth mentioning that Hadoop and many of its products are entirely written in Java. That alone is why this programming language is great for businesses that regularly work with big data.

Scala – This language is part of the Java Virtual Machine ecosystem, and earned its name from being highly scalable. Apache Spark is entirely written in Scala.

See what experts had to say about the four big data programming languages in our latest guide.

3. NoSQL databases

It’s widely known that more than 80 percent of all data generated today is actually unstructured data. For context, most of us normally work structured data that is “tagged” so it can be stored and organized in relational databases.

Unstructured data has no pre-defined structure. Images, audio, videos, webpage text, and more multimedia are common examples of unstructured data. This type of data cannot be worked using conventional methods, which is why NoSQL databases are on the rise.

While there are many types of NoSQL databases, they’re all meant to create flexible and dynamic models to store big data.

4. Data lakes

A relatively new big data technology is called a data lake, which allows data to be in its rawest, free-flowing form without needing to be converted and analyzed first.

Data lakes are essentially the opposite of data warehouses, which make use of mostly structured data. Data lakes are also much more scalable because of its lack of required structured, making it a more optimal candidate for big data.

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Article Credit: G2

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