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.
R – 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.
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.
Data lakes are also built upon schema-on-read models, meaning data can be loaded as-is. Data warehouses are built upon schema-on-write models, which mimics conventional databases. If we’ve learned anything about the world of big data, it’s that conventionality typically won’t cut it.
5. Advanced analytics
Both predictive and prescriptive analytics are types of data analytics that will gain in prominence each passing year. These are considered advanced analytics that will be key for providing insight into big data.
There is currently a variety of predictive analytics software available today. These products analyze historical data from CRM, ERP, marketing automation, and other tools, and then provide future forecasts as what to expect next. Each tool has its own specific capabilities, so it’s worth exploring our category to find one that fits your needs.
Prescriptive analytics goes a step further, taking information that has been predicted and providing actionable next steps. This analysis is extremely advanced and only a handful of vendors today provide it.
6. Stream analytics
With such an influx of big data, both structured and unstructured, analyzing it in real-time has become a real challenge. Stream analytics software is a trending solution for capturing this real-time data as it transfers between applications and APIs.
The rise of real-time analytics means businesses can monitor users and endpoints with more clarity and address issues faster.
7. Edge computing
Internet-connected devices generate massive amounts of unstructured data, making the internet of things one of the largest contributors to the big data universe. Edge computing offers a solution to store this data for quick access.
Edge computing temporarily stores data close to where it was created, hence, the edge. This is its most significant difference from cloud computing.
Edge computing reduces the amount of time it takes information to be transmitted over a network. This can also lead to resource savings.
8. Self-service options
A shortage of data science professionals has opened the door for other ways to analyze big data. One of the more prominent solutions is called self-service business intelligence.
These self-service tools are designed for users with limited technical skills to query and examine their business data in the form of charts, dashboards, scorecards, and other visualization options.
While there are some challenges to self-service, it’s proved to be a great alternative for businesses with limited IT flexibility.
Depending on the industry and business focus, some big data technologies will prove more useful than others. Either way, all the above technologies will in some way help businesses harness and analyze big data will more ease than conventional methods.
Want to learn more? Check out our comprehensive guide on big data to see where the big data market is headed or learn of the importance of big data engineering.
Devin is a Content Marketing Specialist at G2 Crowd writing about data, analytics, and digital marketing. Prior to G2, he helped scale early-stage startups out of Chicago's booming tech scene. Outside of work, he enjoys watching his beloved Cubs, playing baseball, and gaming. (he/him/his)