Big Data As A Service- A Beginners Guide in 2021
1) Scope
With the objective limited to explaining what is BDaaS to those who are just starting in this domain, the scope will be limited to features of big data, BDaaS and major offerings in the market.
To understand BDaaS, you need to wrap your head around Big Data and Big Data Analytics and the benefits it brings to the enterprise. I mention enterprise, as the real benefits of Big Data Analytics is when the data churn is huge. It is not that it is not suited for smaller businesses, but at the enterprise scale, the benefits are magnified.
2) What is Big Data
Big data is the term used for the pool of all data streams that your enterprise generates into one big asset of data be it structured in the form of tables and reports or unstructured in the form of artifacts, images, contracts and so on. There always has been this school of thought, that data is an asset from which you could mine out new findings for efficient business operations or fashion a new revenue opportunity.
Big data is the manifestation of that thought process where it lets you bring all your data into a single unified view so you can take a look at what is really driving your business under the hood, make changes, both tactical and strategic ones, which ultimately impact your bottom line. Big data is a sort of framework that helps you put various components for various kinds of data together, that stream in at various speeds and process this data in an appropriate way, for example, distributed computing, and give you the best insights into your business.
Big data started taking shape in the early 2000’s when industry analyst Doug Laney formulated the three V’s of big data, which are
- Volume:
Businesses and organizations deal with data in all forms, from B2C and B2B transactions to industrial equipment that involves the use of IoT devices, text, videos and images on social media and more. All this data flowing in a medium to large enterprise assumes humongous proportion, sometimes requiring real-time handling and processing, which is not what a traditional relational database based architecture is capable of. With storage technology getting progressively cheaper, it has become easier to store this volume of data in a distributed fashion like in Hadoop(a Big data framework).
- Velocity:
With data from social media and smart systems involving IoT devices or RFID tags, impacting business by the minute, it became very important to set up systems that can process data at much greater speeds than what the traditional systems allow.
- Variety:
With the school of thought that every data asset is important and with data in various kinds of forms flowing through the business, traditional systems lacked the ability to deal with the variety of data sources, like text feeds, videos and images coming from social media, sensor data from IoT devices and the like. The need for processing all data, pushed for systems that could efficiently store and analyse data in such various forms.
Big data architecture is designed to handle the massive data ingestion, sometimes at real-time speeds, processing, the unstructured, semi-structured, structured data and finally making it available for analytical tools to draw insights from at enterprise scale.
3) What is Big Data Analytics
With the data asset and the infrastructure to deal with this data defined, an important component is an analysis that needs to be carried out on this massive data, to gain insights. The toolset used to run statistical analysis and build prediction models is the Big Data analytics part of any big data offering. An advanced form of analytics which involves the application of complex mathematical models and statistical algorithms at massive scales.
- The Importance of Big Data Analytics
Big data analytics can lead you to:
- Identification of new revenue opportunities
- Efficient and effective marketing
- Focused and customized customer service
- Better control of operational efficiency
- Gain a lead in over rivals in the market
What is Big Data as a service
Simply put, BDaaS is an offering, promising you the entire big data infrastructure including big data analytics, on the cloud. BDaaS can be thought of comprising some or all of the below.
- Data sources: various streams of data can be ingested into the Big data infrastructure on the cloud, storing a variety of data, structured, semi-structured or unstructured onto the cloud infrastructure.
- Data Storage: To make match processing easier, data may be stored in distributed file systems with high availability and fault tolerance.
- Batch Processing: Traditional methods of processing data will fail with large and varying types of data coming in at various speeds. Batch processing is the solution for very large data sets.
- Real-time message ingestion: A way to push real-time messages from your real-time data streams like data from IoT devices into the big data systems on a real-time basis.
- Stream Processing: With real-time messages ingested and stored, stream processing is required to prepare the data for further analysis.
- Analytical data store: The processed data is then moved to a data store which supports SQL or NoSQL querying for analytical purposes.
- Analysis and Reporting: Analysis and reporting tools are the tools used to analyze and find patterns, hidden dependencies, predict future outcomes based on various models and finally design and publish enterprise-wide reports.
- Orchestration: In a big data solution all repetitive tasks involving data, like ingestion, transformation, loading of processed data into a suitable data store for analysis and much more, can be automated. This automation is achieved using orchestration tools made available on BDaaS.
Here are a few BDaaS offerings from the big players in the cloud space.
- Google Cloud Dataproc
Google’s BDaaS offering runs Hadoop and Spark on Google Cloud platform integrating BigTable storage and BigQuery analytics.
- Amazon Web Services
AWS offers Hadoop based Amazon Elastic MapReduce on its S3 storage infrastructure.
- Microsoft Azure HDInsight
Microsoft Azure offers Hadoop and Spark on YARN in its own Azure cloud infrastructure.
4) IBM Big Insights on Cloud
Built on Apache Hadoop open source framework, BigInsights is a platform offering BDaaS services with integrated advanced analytical tools and natural language processing engine, Watson.
With the immense amount of data generated in a business on a continuous basis from a variety of sources and in many different forms, BDaaS offers to free up organizational resources by outsourcing the infrastructure and analytical software to experienced players who can offer such services on demand.
Conclusion
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