The phrase “big data” can be traced back to Silicon Valley lunch-table conversations and pitch meetings in the 1990s. It’s a relative term depending on who is discussing it, but one point remains constant: The 21st century has witnessed the greatest explosion of data in history. And that’s why big data platforms and big data consulting became indispensable.
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Up until 2003, the total volume of data recorded was 5 exabytes. In the year 2011 alone, the amount of data recorded was 1.8 zettabytes, which is about 1000x more. Moving forward, it is projected that mankind will produce 463 exabytes of data every day worldwide by 2025. That’s equal to 212,765,957 DVDs each day! Judging from this perspective, we can conclude that the volume of big data produced worldwide is bound to grow tremendously in the future.
In this post, we look at the role of big data platforms in storing and processing huge data sets. But first, let’s give a brief description of big data.
What is big data?
Big data is a term used to describe data of great variety, huge volumes, and even more velocity. Apart from the significant volume, big data is also complex such that none of the conventional data management tools can effectively store or process it. The data can be structured or unstructured.
Examples of big data include:
• Mobile phone details
• Social media content
• Health records
• Transactional data
• Web searches
• Financial documents
• Weather information
Big data can be generated by users (emails, images, transactional data, etc.), or machines (IoT, ML algorithms, etc.). And depending on the owner, the data can be made commercially available to the public through API or FTP. In some instances, it may require a subscription for you to be granted access to it.
Read more about Big data architecture: Definition, processes, and best practices
What is a big data platform?
The constant stream of information from various sources is becoming more intense, especially with the advance in technology. And this is where big data platforms come in to store and analyze the ever-increasing mass of information.
A big data platform is an integrated computing solution that combines numerous software systems, tools, and hardware for big data management. It is a one-stop architecture that solves all the data needs of a business regardless of the volume and size of the data at hand. Due to their efficiency in data management, enterprises are increasingly adopting big data platforms to gather tons of data and convert them into structured, actionable business insights.
Currently, the marketplace is flooded with numerous Open source and commercially available big data platforms. They boast different features and capabilities for use in a big data environment.
Characteristics of a big data platform
Any good big data platform should have the following important features:
• Ability to accommodate new applications and tools depending on the evolving business needs
• Support several data formats
• Ability to accommodate large volumes of streaming or at-rest data
• Have a wide variety of conversion tools to transform data to different preferred formats
• Capacity to accommodate data at any speed
• Provide the tools for scouring the data through massive data sets
• Support linear scaling
• The ability for quick deployment
• Have the tools for data analysis and reporting requirements
Big data platform examples
Here are 6 big data platforms that can help manage petabytes of data and provide actionable insights:
Hadoop is an open-source programming architecture and server software. It is employed to store and analyze large data sets very fast with the assistance of thousands of commodity servers in a clustered computing environment. In case of one server or hardware failure, it can replicate the data leading to no loss of data.
This big data platform provides important tools and software for big data management. Many applications can also run on top of the Hadoop platform. And while it can run on OS X operating systems, Linux, and Windows, it is commonly employed on Ubuntu and other variants of Linux.
It might be interesting for you: MapReduce vs. Spark: Big data frameworks comparison
Cloudera is a big data platform based on Apache’s Hadoop system. It can handle huge volumes of data. Enterprises regularly store over 50 petabytes in this platform’s Data Warehouse, which handles data such as text, machine logs, and more. Cloudera’s DataFlow also enables real-time data processing.
AMAZON WEB SERVICES
Popularly known as AWS, this is another Hadoop-based big data platform from Amazon. AWS is hosted in the cloud environment. Thus, businesses can employ AWS to manage their big data analytics in the cloud. And through Amazon EMR, enterprises can set up and effortlessly scale other big data platforms like Spark, Apache Hadoop, and Presto.
Oracle is another big data platform with a cloud hosting environment. It can automatically send data in different formats to cloud servers without downtime. It can also run on-premise and in hybrid environments. This allows for data transformation and enrichment, whether it’s live streaming or stored in a data lake. The platform offers a free tier as well.
This big data platform acts as a data warehouse for storing, processing, and analyzing data. It is designed similarly to a SaaS product. This is because everything about its framework is run and managed in the cloud. It runs fully atop public cloud hosting frameworks and integrates with a new SQL query engine.
Apache Storm is the brainchild of Apache Software Foundation. This big data platform is used in real-time data analytics and distributed processing. It supports virtually all programming languages because of its high scalability and fault tolerance. Big data giants such as Yelp, Twitter, Yahoo, and Spotify use Apache Storm.
Summary: Big data platforms are here to stay
Enterprises are seeking ways to harness big data and draw actionable insights for better decision-making. This is why they are turning to big data platforms since they provide a one-stop solution for all data needs. They help with capturing, curating, storing, searching, sharing, appraisal, and reporting data insights. Based on your needs, you can choose from the big data platforms that we’ve discussed above. And if you need help along the way, see our big data consulting services. We’re always happy to help!
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