Big Data is the buzzword today. When harnessed wisely Big Data holds the potential to transform organisations for the better drastically. And the wave of change has already started – Big Data is rapidly changing the IT and business sector, the healthcare industry, as well as academia too. However, the key to leveraging the full potential of Big Data is Open Source Software (OSS). Ever since Apache Hadoop, the first resourceful Big Data project came to the fore, it has laid the foundation for other innovative Big Data projects.
According to Black Duck Software and North Bridge’s survey, nearly 90% of the respondents maintain that they rely on open source Big Data projects to facilitate “improved efficiency, innovation, and interoperability.” But most importantly, it is because these offer them “freedom from vendor lock-in; competitive features and technical capabilities; ability to customise; and overall quality.”
Now, let us check out some of the best open source Big Data projects that are allowing organisations not only to improve their overall functioning but also enhancing their customer responsiveness aspect.
This open source Big Data project derived its name from the two Big Data processes – Batch and Stream. Thus, Apache Beam allows you to integrate both batch and streaming of data simultaneously within a single unified platform.
When working with Beam, you need to create one data pipeline and choose to run it on your preferred processing framework. The data pipeline is both flexible and portable, thereby eliminating the need to design separate data pipelines everytime you wish to choose a different processing framework. Be it batch or streaming of data, a single data pipeline can be reused time and again.
An open source Big Data project by Airbnb, Airflow has been specially designed to automate, organise, and optimate projects and processes through smart scheduling of Beam pipelines. It allows you to schedule and monitor data pipelines as directed acyclic graphs (DAGs).
Airflow schedules the tasks in an array and executes them according to their dependency. The best feature of Airflow is probably the rich command lines utilities that make complex tasks on DAGs so much more convenient. Since the configuration of Airflow runs on Python codes, it offers a very dynamic user experience.
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Spark is one of the most popular choices of organisations around the world for cluster computing. This Big Data project is equipped with a state-of-the-art DAG scheduler, an execution engine, and a query optimiser, Spark allows super-fast data processing. You can run Spark on Hadoop, Apache Mesos, Kubernetes, or in the cloud to gather data from diverse sources.
It has been further optimised to facilitate interactive streaming analytics where you can analyse massive historical data sets complemented with live data to make decisions in real-time. Building parallel apps are now easier than ever with Spark’s 80 high-level operators that allow you to code interactively in Java, Scala, Python, R, and SQL. Apart from this, it also includes an impressive stack of libraries such as DataFrames, MLlib, GraphX, and Spark Streaming.
Another inventive Big Data project, Apache Zeppelin was created at the NFLabs in South Korea. Zeppelin was primarily developed to provide the front-end web infrastructure for Spark. Rooting on a notebook-based approach, Zeppelin allows users to seamlessly interact with Spark apps for data ingestion, data exploration, and data visualisation. So, you don’t need to build separate modules or plugins for Spark apps when using Zeppelin.
Apache Zeppelin Interpreter is probably the most impressive feature of this Big Data project. It allows you to plugin any data-processing-backend to Zeppelin. The Zeppelin interpreter supports Spark, Python, JDBC, Markdown, and Shell.
If you’re looking for a scalable and high-performance database, Cassandra is the ideal choice for you. What makes it one of the best OSS, are its linear scalability and fault tolerance features that allow you to replicate data across multiple nodes while simultaneously replacing faulty nodes, without shutting anything down!
In Cassandra, all the nodes in a cluster are identical and fault tolerant. So, you never have to worry about losing data, even if an entire data centre fails. It is further optimised with add-ons such as Hinted Handoff and Read Repair that enhances the reading and writing throughput as and when new machines are added to the existing structure.
TensorFlow was created by researchers and engineers of Google Brain to support ML and deep learning. It has been designed as an OSS library to power high-performance and flexible numerical computation across an array of platforms like CPU, GPU, and TPU, to name a few.
TensorFlow’s versatility and flexibility also allow you to experiment with many new ML algorithms, thereby opening the door for new possibilities in machine learning. Magnates of the industry such as Google, Intel, eBay, DeepMind, Uber, and Airbnb are successfully using TensorFlow to innovate and improve the customer experience constantly.
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It is an operations support system developed for scaling, deployment, and management of container applications. It clubs the containers within an application into small units to facilitate smooth exploration and management.
Kubernetes allows you to leverage hybrid or public cloud infrastructures to source data and move workloads seamlessly. It automatically arranges the containers according to their dependencies, carefully mixing the pivotal and best-effort workloads in an order that boosts the utilisation of your data resources. Apart from this, Kubernetes is self-healing – it detects and kills nodes that are unresponsive and replaces and reschedules containers when a node fails.
These Big Data projects hold enormous potential to help companies ‘reinvent the wheel’ and foster innovation. As we continue to make more progress in Big Data, hopefully, more such resourceful Big Data projects will pop up in the future, opening up new avenues of exploration. However, just using these Big Data projects isn’t enough.
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You must strive to become an active member of the OSS community by contributing your own technological finds and progresses to the platform so that others too can benefit from you.
As put by Jean-Baptiste Onofré:
“It’s a win-win. You contribute upstream to the project so that others benefit from your work, but your company also benefits from their work. It means more feedback, more new features, more potentially fixed issues.”
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What is the objective of doing a Big Data project?
A Big Data project needs to slate out a transparent business goal or objective. Regardless of the products and services, operational efficiency, or any other factor, an objective should be clear. Many organisations feel that they can finish any Big Data project by implementing a technical approach. However, that doesn’t work out in most cases. Since projects that are structured on Big Data are not using the technologies at their maximum, it leads to producing false results. Therefore, to build a Big Data project, your focus shouldn’t be limited to technology. It should focus on the key aspects that will help nurture your business and reach its goals. Also, when you begin to dive into a Big Data project, there are a few issues to acknowledge before you take off. These include lowering logistics expenses, putting marketing efforts, etc.
How can you understand if your Big Data project will work or not?
There are many underlying factors to starting a Big Data project. Constant evaluation is one of them, followed by cost, metrics, and benefits. Thus, it is best to know beforehand how productive your conclusive results will be. If you want your Big Data project to excel, it should be capable of achieving the business objective set against it and provide a measurable Return on Investment (ROI). Having answers to these could help you weigh your forthcoming decisions and their impact on your business.
What is the role of Big Data in project management?
For any project’s success, data collection and analysis play a crucial role. Big Data in any project management can be used to enhance performance. Data can be of great help in planning and delivery that can further assist in making delivery on time. When it comes to cost, data is very expensive. Therefore, your team will use data rather than lose it. All these factors are equal contributors to signifying the role of data in any project.