How to Accelerate the Creation of Data Transformation Pipelines

 In Accelerite Blog

How to Accelerate the Creation of Data Transformation Pipelines

The fierce nature of competition in today’s market has transformed the role of data analytics from a nice-to-have luxury to an indispensable weapon. To thrive in this environment, organizations must utilize data-generated insights to power their business. The faster and easier the process of insight generation is, the more efficient the business will be and the more profits it will generate.

In the early days of data analytics, data pipelines were used to transform and move data from one structured system to another – from a functional system and line of business application to a data warehouse to a data mart. These analytics data pipelines were managed entirely by the IT team. Business teams would raise requests for data and it would be many weeks and sometimes even months before their requests were fulfilled. This would consequently delay decision-making processes, making this method of data analysis viable only for prolonged data projects.

Then dawned the era of the data lake with the promise of using all types of data – structured data from applications and unstructured data from IoT devices, social media, mobile apps – and the ability to run analytics on it faster. The ability of data lakes to support open source frameworks such as Apache Hadoop and Apache Spark sped up the process by allowing organizations to run analytics directly on the lake without moving data into a separate tool. However, the sheer complexity of the data in a data lake and the coding skills needed to analyze this data meant that IT still had to play the role of a gatekeeper. Business teams had to rely on them to get aggregated, joined, pre-processed data that they could visualize. First, IT would prepare, blend and refine a dataset based on the business team’s needs. The business team would then visualize and analyze this data and upon realizing that the data did not reveal any answers, they would go back to IT for more data. After a few repetitions of this process, the business team would receive a satisfactorily refined data set that answered their questions and the cycle would end.

This constant back and forth continues to delay analytics cycles greatly and the issue of dependency remains unaddressed, even in the era of data lakes. Today, the most pressing question facing the evolution of data analytics is – how can organizations eliminate this bottleneck so that data can be independently blended and transformed by business teams without any wait time?

The answer lies in using an end-to-end big data analytics platform such as ShareInsights that enables business teams to blend and transform data entirely on their own without relying on IT, yet in a secure and well-governed manner. Here is a video that we created to show how ShareInsights’ drag-and-drop visual data pipeline designer allows powerful data transformations to be created in record time, without writing any code. The platform is designed for high-performance insight creation on even the largest of datasets. By facilitating self-sufficiency in the building of a data transformation pipeline, enterprises can drastically reduce data analytics bottlenecks and get the insights they need, exactly when they need them.

Watch the video below to see how you can join datasets, apply transformations, and build a data pipeline from scratch in seconds to enable faster insight generation.

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