How ETL tools Connect Development & Analysis Teams?

Posted By: administrator
Posted On: 10 Nov, 2020
Last Updated On: 19 Nov, 2020

Organizations divide between corporate groups, such as sales, marketing. In the data-driven companies, the data development team are on one side, and Data analysts are on the other. The development team regulate ETL tools, data streams and sources, whereas the analysts are work closely with decision-makers to provide deeper insights based on relevant data. 

Modern businesses are becoming data-oriented; hence, data analysts are under constant pressure to provide company management with actionable insights for better and informed business decisions. They have to depend on data development teams for relevant data for effective analysis. This approval process is often difficult and time-consuming. Hence, by that time business needs which are dynamic keep on changing.  

There is a recent survey of data analysts conducted by Fivetran named Dimensional Research. The study shows that 62% of the participating data analysts reported waiting on centralized development teams for analysis-ready data “several times per month.” They have to go through several redundant processes to access critical data. This delays data analysis, also, business decisions. Many analysts actually cannot dedicate their time and effort on profit-generating ideas because of this unnecessary delay. 

How ETL tools lift the burden off the Data Engineering Team?

The primary hurdle is that data-development teams have to spend an extensive amount of time on building and maintaining data pipelines. Automated, zero-configuration cloud data pipelines offer three essential functionalities that reduce this burden of data engineers:  

  • Automatic data updates: Automated ETL tools can easily identify updates in data sources. Modifications can be insertion or removal of new records into a source database. ETL tools automatically detect the updates and send them to the target destination, which can be a data lake, data warehouse or centralized analytical data stack.  
  • Automatic schema migration: A data pipeline automation tool must be able to detect when the changes mentioned above are happening to a data source’s schema. The edits can be adding or removing columns, modifications to a data element’s type, new or deleted tables/objects.  
  • Pre-built connectors: Automated data pipelines comprise of a wide variety of pre-built connectors for a wide array of files and file types, databases, applications and SaaS services. 

Uniting Development & Analysis Teams

With the widespread use of automated data integration or ETL tools, there won’t be any conflicting relationships between development and analytics teams. Data analysts will get the specific data required and without any delay or cumbersome approval processes. As a result, there will be rapid delivery of insights to decision-makers based on real-time data. Development teams will also be free of hassles because they can focus on building out core data infrastructure instead of spending development cycles maintaining ETL tools.

There are several ETL tools in the market, choose the one fir for you business:

  • FivetranFivetran developed zero-configuration, zero-maintenance data pipelines to deliver data into modern cloud warehouses and accelerate analytics projects.
  • Stitch DataStitch provides a powerful ETL service designed for developers. Stitch connects to all data sources and SaaS tools to replicate all relevant data to a data warehouse.
  • BlendoBlendo is a cloud-based ETL/ELT solution that facilitates cloud data integration to enable smart Business Intelligence initiatives.
  • PanoplyPanoply is an end-to-end data integration tool which provides the tools for data integration, connecting, transformation, warehousing, etc. Panoply delivers unified, ELT and Smart Data Warehouse, simplifying the journey of migrating raw data to analytics using machine learning.
  • DatonDaton is an effective data integration tool that would seamlessly extract all the relevant Data from popular data sources then consolidate and store it in the data warehouse of your choice for more effective analysis. The best part is that you can use Daton without the need for any coding experience and it is the cheapest data pipeline available in the market.

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