Do you think a data analytics platform is a necessity in today's world? If you already have a data analytics platform, then do you think it's sufficient? Firstly, it is important to examine your existing data analytics platform and secondly, if you do not have one, then build a cloud data analytics platform.
There are multiple data analytics courses available in the market. The postgraduate programme in data science and analytics by Imarticus will teach you how to effectively build a platform that will integrate the characteristics of cloud and data analytics. Go through this piece to know all about the needs of a data analytics platform and how to successfully build one.
What is data platform?
An organisation's data is stored and processed centrally on a data platform. The collection, transformation, and application of data for the purpose of producing business insights are handled by a data platform. Notably, data platforms have been embraced by data-first businesses as a successful means of aggregating, operationalising, and democratising data at scale throughout the business.
Layers of a data analytics platform
For your better understanding, the categorisation of six essential layers of the data platform has been provided so that you did not miss out on any of them. The essential layers are enumerated as follows:
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Data integration: With increasingly complex data infrastructure, there is a need for a platform that can handle the complexity of data. It is a challenging job for data teams to work with unstructured data and induce structure in them. In the context of extract transform load (ETL) and extract load transform, this is frequently referred to as the extraction and loading stage (ELT).
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Data storage and processing: It is a must for a data analytics platform. Data warehouse versus data lake or even a data lakehouse, which one offers more accessible and affordable options for storing data in comparison to many on-premises solutions, have emerged as a result of businesses shifting their data platforms to the cloud.
Data transformation and modelling: Although data transformation and modelling are two distinct processes, they are frequently used interchangeably. When you transform your data, you take raw data and use business logic to clean it up so that it is suitable for reporting and analysis. By modelling data, you may produce a visual representation of the information that will be kept in a data warehouse.
Business intelligence and analytics: The information and collected data can become useless if your team or workers do not know how to use it. The business intelligence (BI) and analytics layer would be the cover of the data platform. Without this layer, your data lacks meaning and cannot be used for intelligent decision-making.
Data observability: It is a company's potential to completely understand and observe the data it has collected. If you do not know how to observe data, then there is no point in working on it. Incorporating DevOps best practices into data pipelines helps avoid data downtime and make sure the data is useful and usable.
Data discovery: It fills in the gaps left by many standard data catalogues, such as their frequent manual nature, lack of scalability, lack of support for unstructured data, etc. If data catalogues are a map, then data discovery is the GPS on your smartphone, updated and improved all the time with the most recent facts and figures.
Conclusion
These are the major requirements that you must fulfil to build a successful cloud data analytics platform. Some major data analytics tools that can help you in this regard are Microsoft Excel, Tableau, Apache Spark, RapidMiner, etc.