Automated Data Integration: 5 Steps to Consolidate Data in a Data Warehouse

Posted By: administrator
Posted On: 07 Nov, 2020
Last Updated On: 19 Feb, 2021

A fully-managed cloud data warehouse might not always be fit for your business. For effective data integration, choose the right tool for your company following these 5 simple steps –   

  • Make a thorough evaluation of the business needs 
  • Decide whether to migrate or buy a new one
  • Assess cloud data warehouses and business intelligence tools
  • Decide which data integration tools to use
  • Calculate the total cost & ROI

Evaluate Your Business Needs

Many businesses do want to outsource their data operations to a third-party service provider. The primary reason being the size of the organization. If the business operates with a very small scale or minimum complexity of data, then it would not make sense for them to invest in a cloud data warehouse. They might not have data operations; only use one or two applications and the integrated analytics tools used by them for each application are already sufficient.  

Another factor not to purchase a modern data warehouse can be the performance or regulatory compliance standards. If the latency of data replication, consistency of data affect business operations immensely, one might want to avoid third-party cloud infrastructure and build their system.  

But for mid-size to large scale businesses with sufficient maturity will harness the power of data analytics, by investing in cloud data warehouses for automated data integration. 

Migrate or Build a New One

The service providers who perform automated data integration can migrate data from a traditional system to your new data warehouse. But this task is quite a hassle based on the complexity of the data. The decision to migrate or building a new system from scratch immensely depends on what volume of historical data is essential for your business.  

If your company has already purchased data integration tools or services, it might not be helpful to end those contracts, especially when a lot of money is involved.

Evaluate Cloud Data Warehouse and Business Intelligence Tools 

You have already assessed what your business requires, now start comparing different data warehouse solutions based on features your company needs in a cloud data warehouse and business intelligence tool. The vital functionalities to consider while choosing a data warehouse include: 

  • Type of data storage 
  • Concurrency 
  • Data governance & management  
  • SQL dialect  
  • Elasticity 
  • Backup and recovery support  
  • Resilience and availability  
  • Security  

Business intelligence tool should include the following features: 

  • Seamless integration with cloud data warehouses  
  • Drag-and-drop interfaces. 
  • Data ingestion and exporting data files.  
  • Ability to conduct ad hoc calculations and reports. 
  • Automated reporting and notifications. 
  • Speed, performance, and responsiveness.  
  • Modeling layer with development mode.  
  • Extensive library of visualizations.  

The cloud data warehouses and BI tools that you will be using for your business should be compatible with each other. 

Evaluate Data Integration Tools

There are many essential functionalities to consider while choosing data integration tools: 

  • Customization and configuration. 
  • Reliability and performance of the software.  
  • Quality and responsiveness of customer support teams. 
  • Ease of use and accessibility. 
  • Number and type of data sources covered.  
  • Costs and payment plans.  

Make sure that you compare and then buy the data integration tool you require. There are many reviews and rating platforms for data integration tools that should be mutually compatible with the data warehouse and business intelligence tool. 

Calculate Total Cost and ROI

Modern cloud data warehouses promote savings of time, money, and labor. Compare your current data integration workflow with a different competition in the market.  

Perform a thorough audit and calculate the cost of your current data pipeline before investing in data integration activities. The costs you need to consider are costs of configuring and maintaining, opportunity costs incurred by failures, stoppages, and downtime. There will also be subscription costs for your data warehouse and BI tool.


Now, you have evaluated all the popular data integration tools in the market based on important checkpoints. Most of them will offer free trials for a few weeks. 

Begin by setting up connectors between your data sources and data warehouse. You will be able to estimate how much time and effort it takes to consolidate your data. Perform some fundamental data transformations. Compare the results of the trial runs against your business goals. 

The basic criteria of the data analytics after employing a cloud data warehouse for automated data integration should be: 

  •  Reduced time, labor, and cost compared to the traditional approach.  
  • Expanded capabilities of the data team.  
  • Successful completion of new data projects. 
  • Reduced turnaround time for reports. 
  • Reduced data infrastructure downtime.  
  • Higher rates of business intelligence tool adoption. 
  • Exploring new metrics that should be actionable.

Daton is an automated data integration tool that extracts data from multiple sources for replicating them into data lakes or cloud data warehouses like Snowflake, Google Bigquery, Amazon Redshift where employees can use it for business intelligence and data analytics. It has flexible loading options which will allow you to optimize data replication by maximizing storage utilization and easy querying. Daton provides robust scheduling options and guarantees data consistency. The best part is that Daton is easy to set up even for those without any coding experience. It is the cheapest data pipeline available in the market.

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