Data Pipeline Automation

Why should we automate these data processes?

The primary reason to automate data encapsulating is to save businesses time and money. Rather than manually performing data entry tasks, an automation system can help ensure that business databases are always up-to-date. This ensures that businesses will have access to accurate and timely information, which will allow them to make better decisions. Imagine a company that needs to process 1 million data points to generate a report by the end of the week. The company would have spent all of its money, time, and resources on people who need to analyze this data. Automating this data pipeline would free up manpower to perform other necessary tasks and save money on hiring more people or outsourcing work.

A secondary but essential reason is that automating data pipelines provides insight into the data. Specific analytics tools offer data profiling, which lets you see the percentage of empty cells in columns and rows, the distribution of values in columns, and the uniqueness and relevance of values for given columns. This helps you identify any rows or columns with missing or erroneous data points which might have been missed during extraction or provide initial insight as part of the data analysis.

Modern enterprises use different apps to manage their business functions. HR functions may be using Workday, Officevibe for payroll and engagement, the sales may rely on Salesforce and MongoDB, Marketing on HubSpot, and Marketo for automation. All these systems operate in silos. If you want to identify the best customer group and how to serve them, you need to filter all of them.

Data pipelines ensure that the data remains consolidated into a common destination from all these disparate data sources. The data pipeline also enables data with consistent quality. Most importantly, you can accelerate your time to insights for quick decision-making.

Data Pipelines – The common architectures

Batch data pipelines

If you want to move data at a specific time, batch data pipelines are the best choice. They trigger significant amounts of data as per a predefined threshold or react to a particular data flow behavior. ETL processing leverages these pipelines and is primarily used for standard BI reports.

Streaming data pipelines

Mainly used for real-time data processing, these pipelines move data once it starts creating. In other words, they are used in data lakes as part of data warehousing integration. Also, preferred for real-time ML use cases like recommendation engines to integrate, transform and feed into the real-time ML algorithms to generate the best product recommendations.

Benefits of Data Pipelines

Speed

Data pipelines should be capable of transferring data faster for accurate business insights. The entire data ecosystem may not be competent to handle real-time data processing all the time.

Flexibility

Data pipelines should be able to handle the changes at a swift pace. Any change in the APIs or data sync-up issues may lead to inaccurate insights. Also, there might be significant lag while accommodating the changes. Data pipelines need to be flexible to handle these changes.

Operational efficiencies

A central IT team usually manages the data pipelines. Also, the data processing is centralized. Collectively, this may lead to inaccuracies and a lot of effort. Establishing a team for data pipelines comes with a price tag. Instead, if different can leverage pre-built data pipelines according to their needs, organizations can witness significant improvement in operational efficiency.

The solution

Automated data pipelines provide the needed solution for all these challenges. You can leverage the easy-to-use interface with automated data pipelines to transform the data in minutes.