Big Data: Today's Top Tools

Big Data: Today's Top ToolsIf you’re looking to pursue a career in big data or embark on a data science degree, there are a number of big data tools that can help you stay ahead of the curve in this rapidly advancing field.

The market is being flooded with tools that promise to provide business value by analyzing data for key insights. Innovations like machine learning, the internet of things (IoT) and “dark data” may disrupt the big data status quo, but the tools and technologies listed below could have great impact on the fields of data science and data analytics.

First things first: even if you’ve only dipped a toe into the big data world, you’ve likely heard of Apache Hadoop. The open-source software suite is used for the distributed storage of large datasets on computing clusters. Its MapReduce framework allows programmers to write applications that process the data in massively parallel fashion. Not surprisingly, Hadoop is a huge player in the world of big data – and so many of the following tools interact with its components in some way.

AWS, Dataproc and Azure

All of this data processing requires access to vast storage and computing resources, which makes Hadoop (and similar frameworks) a good match forpublic cloud providers that can provide virtually limitless amounts of both.

These include Amazon Web Services, Google Cloud Dataproc and Microsoft Azure. These “Hadoop as a service” providers can offer levels of scalability that more specialized big data solutions cannot – giving them a clear advantage as enterprise data stores continue to grow exponentially.

Spark

Hadoop’s MapReduce was a game-changer when it came to processing large datasets quickly, but Spark is catching up in popularity. Both are cluster-computing frameworks developed by Apache, but whereas MapReduce processes data on disk, Spark processes it in memory, making it up to 100 times faster.

This is particularly useful for companies that want real-time, immediately actionable data analytics. It also makes Spark ideally suited anywhere large volumes of data need to be processed on the fly, such as machine intelligence and the IoT. This makes it a good choice for the data scientist who needs to perform cutting-edge data analytics.

Hive and Pig

Built on top of the Hadoop platform, Hive allows users to analyze large datasets using HiveQL, an SQL-like query language. It allows analysts to read, write and manage large datasets that reside in distributed storage. It can directly utilize several different big data frameworks to execute queries, including Spark and MapReduce.

Pig is similar to Hive, offering a database query framework that allows users to manage and filter datasets much quicker than if they had to program at the MapReduce level directly. Pig does however allow more flexibility than Hive when working with datasets that have not yet been fully structured. To master Hadoop, it’s recommended data scientists acquire skills in both Hive and Pig.

MongoDB

MongoDB is an open-source NoSQL database package that includes dynamic schema capability. This makes it a good option in situations when dealing with data that is constantly changing, semi-structured or unstructured. Since its data model allows users to store and combine any type of data, it is often used to aggregate data into a single view from multiple sources. This can include data previously not utilized, also known as “dark data.”

One of the areas where MongoDB is useful is in omnichannel retail, where customer and product data are often distributed across mobile apps, product catalogs, CMS databases and content management systems.

Tableau

Unlike the previous items that focus on analysis, Tableau is a data visualization tool that mainly focuses on delivering visual business intelligence (BI) for nontechnical business professionals. A leader in the self-service BI space, it allows you to create bar charts, maps, scatter plots and more without having to write code. Attractive, interactive visualizations can be created using its drag-and-drop interface. Data sources can include relational databases, unstructured datasets and higher-dimensional data arrays (or “data cubes”).

While this isn’t an exhaustive list, it is a rundown of the top tools for mastering big data in 2017 and beyond. New data sources are added every day and advances such as machine learning are coming of age, so there’s never been a better time to brush up on your big data skills.

Learn more about data science careers including skill building and specialization options.