Importance and evolution of Data engineering in building analytics pipelines
Analytics and data engineering are two of the most widely used terms in today’s world and both are inevitable for each other and self-dependent. It’s very important to understand what Data engineering is and how it evolved for last few years and how important it is to the Analytics world.
Why Data Engineering?
Back in the days and even in today all data extraction, load and transformation (ETL) are either done by shell scripts, an enterprise ETL technologies such as Informatica, IBM DataStage, SSIS and many more. These enterprise level technologies worked very well structure/tabular data where data volume is small to medium. These technologies have predefined sources which are used to extract data, transform while data being flown through the application and load to any databases or file system. In last few years, we have witnessed some drastic changes the way data is being generated, in short three V’s changed the world. Data velocity, variety and volume, “3Vs” lead to the innovation of data engineering which primarily evolved from software engineering keeping in mind using the same process with flexibly using different programming languages. But although Data engineering is used in several ways today, they all are related to extraction, transformation and load of large volume of different unstructured, semi structured and structured data.
Creating a superior analytics environment:
A superior analytics environment of comprises of both the aspect of business queries to optimize currently existing business process as well as provides capabilities for business to evaluate the future state of the program in order to strategically oriented organization. Analysis of the current business processes can be done through traditional data warehouse and data lake can be leveraged to build predictive models using unstructured data that can be ultimately used for making those strategic business decisions. Having data warehouse and data lake complements the organization’s ability to access useful information from their disparate data sources and structures.
Let’s discuss what is data lake and data warehouse and why they contribute to an efficient analytics platform. Data lake is the repository of raw data which can be anything, structured, semi structured and completely unstructured. But all these data are pulled together and stored in a single repository where it can be easily accessible. In contrast, data warehouse is a structured, filtered and “may be” aggregated data which has a defined purpose. Data warehouse is a single source of historical and current information of business transactions and processes. Data lake is often heavily used by the data scientists for building scoring or prediction models of advanced analytics compared to data warehouse which is built for business organization to gain actionable insights of business processes.
Which one should be right for you:
Having a data lake, data warehouse or both can be an important decision in your journey to the data analytics world. It depends on the type of organization you belong to and the nature and consumption of data that your organization is currently has. An example of data need specific to organization is healthcare industry which has been using data warehousing for many years successfully but in recent years the improvement of modern healthcare technologies generating huge amount of unstructured data created a need to store in data in a single repository to prevent data silos and leverage. The data for advanced healthcare analytics. Another example about the specific nature of consumption of data can be seen in manufacturing industry which typically has diverse types of data such as data from IoT sensors, data generated by the machines, unstructured data such as images and traditional structured data from the CRM and ERP applications. To store the data of this much volume and variety we need data lake as well as data warehouse. But if your data journey did not start from traditional route and do not have a data warehouse then are different path to your data journey. One path would be build a data lake where all form of data be stored and use modern sophisticated technologies to query the data directly from the data lake and this would be the faster of more agile approach where as traditional data warehousing even those staging on data lakes would be more process oriented, slow and cautious approach that may or may not suite your organization. But keeping in mind, data lake is not free from complexity and difficulties maintaining a cohesive data platform.
These are the basic criteria starting your journey but more substantial discussion regarding your actual data need can be done. Call us to know more about what suites your company.