Choosing the right approach for your enterprise data pipelining process - The EE

Choosing the right approach for your enterprise data pipelining process

As businesses continue to expand and adopt new technologies (for example, adopting new IoT applications), there is an explosion of data from various platforms. Today, businesses are in dire need of streamlining enormous data to get valuable insights. The data analytics market reveals that this is the right time for businesses to explore data analytics capabilities to improve business intelligence processes, says Yash Mehta, an IoT and big data science specialist.

As per the survey conducted by the BARC research report, businesses using big data saw a profit increase of 8% and a 10% reduction in overall costs. Data from disparate sources should be integrated (on a single location) for easy access and seamless analysis (Data Pipelining).

The different data pipeline approaches facilitate the movement and transformation of data to a centralised location (Data Warehouse). Bain & Company conducted a study of over 400 businesses that revealed that the businesses with the most developed data analytics capabilities have a larger market share. Also, it was inferred from the study that such businesses are twice as likely to be in their sector’s top 25% for profitability and five times more likely to make more prompt decisions than competitors.

Choose the right data pipeline approaches

Enterprises should choose different approaches, depending on the heterogeneity, complexity, and volume of data sources involved. 

Let’s look at the most popular Data Pipeline approaches.

  1. ETL (Extract, Transform, Load), a traditional data pipeline approach works as an intermediary process by transferring data from data sources into the target destination or a Data Warehouse. In ETL, the data is extracted from different sources and formats into a staging space (testing server). Next, the data is transformed to avoid any potential errors due to variation in data. Once the data is transformed and sanitised, the data is loaded into the target location in batches.

Some common scenarios when this approach is considered are when the volume of data is structured, small and complex, or the data is utilised for the BI tools and requires sanitised data for preparing customised dashboards and business. To locate and fix any issues/errors in the data pipeline is cumbersome with the ETL approach. A data analyst has to redo the entire process of extracting, transforming, and then loading.

  1. ELT (Extract, Load, Transform), is an evolved version of ETL. Though the initial stage of ELT works the same way as the ETL stages, i.e., the extraction of data from various sources, the placement of “T” in the acronym ELT makes the data pipeline approach much simpler and quicker as compared to the ETL approach. In ELT, data is not transformed before loading it into a data warehouse. Thus, data scientists have access to raw data that increases the scope for better and valuable analysis.

This approach is considered the right choice when quick access to available data is required. ELT works well with large volumes of data, whether structured or unstructured. Though the approach is time-saving, the data in warehouses is raw and can contain sensitive information. Data present in ELT warehouses isn’t as reliable as an ETL data warehouse can attract compliance issues with the data privacy regulations (CCPA, GDPR, HIPAA, etc.). Thus, data monitoring and governance can become a hassle with this approach.

  1. eETL (Entity-Based Extract, Transform, Load), is a brand-new approach where data is collected, processed, and delivered for every field (business entity, customer name, address, etc.) as a complete, clean, and connected data asset.  Thus, the extraction of data happens for a particular entity from all source systems. 
Yash Mehta

The eETL approach offers the best amalgamation of both worlds (ETL and ELT), as it supports a massive amount of structured/unstructured data. It is also compatible with data privacy regulations before loading it to the warehouse. Also, since the data transformation step is at the field (entity) level, there is a holistic view of data available for businesses. 

Data pipeline tools 

After understanding the data pipeline approaches, the discussion in most business meetings is the Data Pipeline tools available in the market. Some of the Data Pipeline tools to consider for obtaining trusted and valuable insights in enterprises of any size are:


K2View data pipeline tool is a modern and popular data pipeline tool. It enables a trusted data pipeline process and supports a massive collection of data from all sources to all targets (data warehouses). It also ensures that the data is compliant with data privacy regulations while delivering real-time insights (data is always up to date).

It manages data from disparate sources in any technology or format and models the data fields for business entities (e.g., customer, location, device, product). Next, this data is ingested into micro-databases. Later, other data processing steps like data masking, transformation (uses an in-memory database to perform data transformation at high speed), and enrichment are performed. Finally, this integrated data is sent to the consuming applications. Some of the popular enterprises leveraging this tool are Vodafone, Rogers, AT&T, etc.


Talend is a unified platform tool following sophisticated ETL procedures. It offers a range of in-built connectors. It connects, extracts, and transforms data from various sources (cloud and on-premises) and turns that data into valuable insights. It offers data management and monitoring capabilities. Talend acts as a data integration platform primarily designed for the new Big Data and cloud-centric enterprises. Talend is a more complex tool and requires technical expertise to connect. Some of the popular enterprises leveraging this tool are Dominos, Danamon, etc.


Blendo is another ETL and ELT data integration tool that enables enterprises to integrate the data from different data sources into a central location. This tool builds new connectors and maintains the existing ones, which makes the ETL process smoother and faster.

Thus, it helps to reshape, connect, and deliver actionable data. It provides a fast way to replicate data from applications, databases, events, and files to cloud warehouses such as BigQuery, and Redshift. It creates analytics-ready tables and schemas for analysis with any BI software. Some of the popular enterprises leveraging this tool are Checkr, Instamojo, etc.

The author is Yash Mehta, an IoT and big data science specialist.

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