Big data workloads are those that involve the processing, storage, and analysis of large amounts of unstructured data to derive business value from that data. Traditional computing approaches and data processing software weren’t powerful enough to cope with big data, which typically inundates organizational IT systems on a daily basis.
The widespread adoption of Big Data analytics workloads over the past few years has been driven, in part, by the open source model, which has made frameworks, database programs, and other tools available to use and modify for those who want to delve into these big data workloads.
A particularly important family of open source projects for big data purposes have emerged from the Apache Software Foundation. This open source community has released big data projects such as Hadoop, Kafka, and Spark, all under the Apache License.
An important idea underpinning open source is that it encourages a large community of developers in different locations and from different companies to work together to progressively improve projects. Furthermore, if individual developers or data scientists encounter difficulties when using these open source tools, there is nearly always someone available from the community to help them out.
The release of big data tools as open source is good news for companies looking to get value from all this data. Open source provides ready-made, high-quality code for big data purposes; code which would be time-consuming, complex, and expensive to develop from scratch.
It’s important, however, to bear in mind the security concerns of using open source frameworks, libraries, and other components for any type of workload, and that includes big data. Security is a real challenge with open source, and several high-profile data breaches of recent times, including the infamous Equifax breach, have resulted due to a combination of negligence and open source vulnerabilities.
Therefore, companies need to understand how vital open source vulnerability management is to ensure a high level of security when using open source code.
- Apache Beam
Apache Beam provides a unified programming model and language-specific SDKs that enable developers to easily define and execute data processing pipelines. In the context of big data, two types of processing that this model simplifies are batch and stream processing.
Batch processing is done when you want to process a collection of data points that have been grouped together within a certain time interval. Streaming processing deals with processing continuous data, such as the data collected by a sensor that detects temperature. As long as that sensor works, it will output data that needs processing.
What’s so exciting about Apache Beam is that you only need to build a single, portable pipeline that you can do either batch or stream processing with. The ability to move your pipeline between different processing frameworks gives excellent flexibility and agility so that you can reuse a single data processing pipeline for many different use cases.
MongoDB is an open source NoSQL database program that is often used in Big Data workflows. Because data can be stored in any format, it works really well for real-time big data analytics, and it is scalable across the type of distributed computing structures that are necessary for big data processing, storage, and analytics. MongoDB lets you easily store growing, unstructured data like customer preferences and add different types of data.
MongoDB helps users get analytic outputs from Hadoop into their operational apps. With MongoDB, you can perform real-time ad-hoc queries and aggregations on big data with extremely low latency.
Apache Spark is an analytics engine for big data processing, and it empowers you to process batch and real-time data at blazing speeds. You can run Spark in a standalone mode by running the framework and a Java virtual machine on each machine within a big data cluster.
Spark’s impressive speed for certain workloads is owed to its excellent in-memory computing capabilities, which processes data in faster RAM memory rather than using hard disks. You can also perform interactive processing and graph processing, hence Spark’s reputation as a powerful general purpose computing engine.
Spark can be configured to run on top of a Hadoop YARN cluster, so that you can use it with massive datasets in a distributed computing environment.
Cassandra is another NoSQL database that differs quite markedly from MongoDB, even though they both fall under the same database system category. Cassandra is a solid open source option for managing and handling huge amounts of data across many commodity servers with a fault-tolerant structure so that there is no single point of failure. What this means is Cassandra delivers continuous uptime.
You can easily deploy Cassandra on multiple servers, so it has very good scalability out of the box. In a big data architecture, you could use Cassandra for final storage, so that end users such as data analysts or data scientists can perform extremely fast queries on the data. You could also use Cassandra for raw data storage.
Tensorflow is an open source machine learning framework that you can combine with your big data to get more advanced analytics. You can run TensorFlow on Hadoop clusters and combine it with a powerful computing engine like Apache Spark to perform predictive analytics and other exciting machine learning use cases.
Open source and big data are inextricably linked, and if you work to prevent security issues using adequate open source vulnerability management, you can take full advantage of the growing range of amazing open source projects that let you get the most from all the data you collect.