ETL Automation

Setting up an automated data pipeline so data is always available and reliable

The fastest way to set up scalable data infrastructure.

Everything you need to organize your data and prepare it for the BI tool of your choice.

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In the contemporary business environment, computer automation has brought about a remarkable surge in productivity, but also in data. Virtually every business now relies on a number of tools — product databases, payment solutions, CRMs, inventory management systems, ad platforms, etc. — to operate and drive revenue. 


These tools typically have some built-in reporting capabilities, but those features can’t support combining data from multiple sources for more complicated analysis, seat limits might interfere with who has access to what data, and they aren’t designed to share reporting across an organization. The result is “data silos” — many separate sources of information that aren’t integrated. As a result, businesses struggle to share insights and make consistently data-driven decisions, even if they’d like to. 


The result is typically some combination of copy-and-pasting from many CSVs, broken Google Sheets automations, and giving up on all but the most critical of data projects. 


While manual data syncing from various sources may be feasible, it comes with a price – consuming valuable time, resources, and compromising data quality due to the risk of errors. The associated expenses related to data synchronization, translation, and human errors have prompted enterprises to seek innovative software solutions. This is where ETL automation tools come into play, offering compelling remedies to these challenges.


ETL (Extract-Transform-Load) tools extract data from these siloed sources, clean the data and organize it as needed, and load it into a data warehouse, a centralized repository for storage and analysis. When automated, these tools can become particularly valuable, as they can help your entire team access the data they need to make decisions, with less time-consuming and error-prone manual work. If your company grapples with data silos, consider embracing ETL automation tools as the ultimate solution.

What Is ETL Automation?

Inefficient and error-prone manual data management tasks can be a major hindrance to businesses. Imagine the complexities involved when merging data from diverse teams like sales, marketing, customer service, and accounting. But fear not, ETL automation comes to the rescue, granting you complete control over the process and the flexibility to schedule automated tasks.


To unlock the full potential of your business intelligence (BI) tools, a unified database source is essential. Mozart Data simplifies this process by breaking it down into three key steps:

  • Extract: Seamlessly extract data from each group.
  • Transform: Clean, reformat, and/or merge the data while resolving differences in conventions and schema.
  • Load: Load the transformed data into a data warehouse, creating a harmonized source of truth.


How does this work in practice?


Leverage the power of Mozart’s platform to streamline your data extraction process effortlessly. Extracting data from databases and third-party tools becomes a breeze, eliminating the need for engineering resources to code custom interfaces. Once extraction is set up the process can be automated. Schedule your data to sync when you need it, so your data is always up-to-date for reporting and analysis, without wasting resources. 


By embracing automation for data extraction, you can unlock significant time savings and mitigate risks associated with manual extraction methods.


To derive meaningful insights from your BI tools, high-quality and well-organized data is paramount. Mozart enables you to automate a substantial portion of data work, leading to faster and easier data analysis. Our data transformation process includes data cleansing, ensuring that all data meets the highest quality standards. This involves addressing issues such as missing values, misplaced entries, typographical errors, incorrect or inconsistent dates, and phone numbers.


The final step involves loading data into a unified database (in this case a data warehouse), which encompasses the amalgamated and cleansed data from relevant sources within your enterprise. Notably, data transformation can occur after loading, expanding the ETL acronym to ELT or ETLT, providing greater flexibility and efficiency.


While some businesses may attempt ETL automation using Python, R, or other programming languages, Mozart offers a cutting-edge modern data stack. This stack incorporates a data transformation layer built on a user-friendly SQL editor, catering to non-technical users with ease and simplicity.


ETL Automation Tools

To automate your warehouse data processes effectively, you require a set of efficient data integration tools that seamlessly move data through each step of the ETL process – from extraction to translation, and finally loading it into the data warehouse. Mozart’s platform boasts key tools like Fivetran and Portable, which play a crucial role in streamlining data operations.


Some companies turn to open source tools. While open source data integration tools offer customization options, they may add complexity and demand technical expertise. Unless your organization has a team of data engineers on hand to manage open source tools and the connections between them, we don’t recommend them. 


At Mozart Data, we offer an out-of-the-box modern data stack, complete with built-in integrations that automate connections between tools. This approach simplifies the process, accelerating data transfer, and avoiding silos, ensuring seamless data processing.


When it comes to effortlessly moving data to and from a data warehouse, Fivetran stands out as the best tool in the market. Boasting over 300 connectors, it comprehensively covers most common data sources, ensuring smooth data transfers. Mozart Data relies on Fivetran as a key component of its platform, often complementing it with other ETL tools for optimal results.


Have a data source that Fivetran doesn’t support? We also utilize Portable for custom data connectors, so you never struggle to integrate data that’s critical to your business. 


Mozart Data is built on top of Snowflake data warehouses, providing customers with a secure, reliable central location for their data. Snowflake can also support additional data transformation, AI and machine learning tools, and more. We chose Snowflake because we believe it’s best-in-class for whatever your specific data needs are. 

ETL Automation Framework

Data tools are better together — the power of combining multiple tools in one cannot be understated. Mozart Data’s modern data platform offers seamless integration and advanced automation capabilities, allowing you to harness the strengths of different tools with ease, creating a formidable data processing arsenal. 


This collection of top-notch tools forms an ETL automation framework or platform, streamlining data processing activities, and creating a centralized source of truth for your business’s data. The end result is an automated data pipeline that puts data in the hands of the people who need it. 


ETL In Business Intelligence

In the competitive business landscape, business intelligence plays a pivotal role in gaining valuable insights across all operational facets. However, to obtain the kind of results critical for making informed decisions, business intelligence tools rely on high-quality, complete, and accurate data.


Implementing ETL in business intelligence enables you to efficiently collect data from every department within your enterprise. Whether it’s sales, marketing, customer support, finance, manufacturing, or others, ETL allows you to consolidate the required data into a universal source of truth, providing a single, comprehensive view.

Why Automate?

Manual data management tasks are time-consuming, tedious, and error-prone manual data collection processes. Automating processes is a game-changer for your business intelligence tools, ensuring you receive the highest quality data instantly. Encourage your employees to prioritize data quality by providing them with user-friendly tools that simplify the data collection process, fostering a self-serve data culture. A burdensome process can lead to reduced participation and hinder valuable insights.


Automated ETL processes bring remarkable value compared to manual methods, with users reporting up to five times faster processing through automation. By adopting easy-to-use and automated ETL processes, your employees can focus on high-value tasks, freeing them from

Benefits of Automated ETL Processes

Automated ETL processes not only save valuable time and resources but also guarantee superior data quality. Through data cleaning techniques such as removing duplicates and replacing invalid or missing data, you can ensure the utmost data accuracy. By consolidating pristine data from all pipelines into a single universal source of truth, you gain a comprehensive dataset, delivering consistent and reliable insights via your business intelligence tools. Embrace the Mozart Data ETL platform to implement your ETL data strategy, significantly saving time, and enhancing the data quality of your BI insights.

ETL Testing Automation Ideas

What is ETL testing?

In the realm of ETL data pipelines, prevention is often the best remedy. A comprehensive data strategy should encompass data testing to ensure the credibility, authenticity, and reliability of your BI insights. The accuracy of charts, graphs, and reports generated by your BI tools relies on the quality of data feeding into them. To tackle this critical aspect, forward-thinking businesses are adopting automated testing using an ETL automation testing framework.


Traditional ETL Testing Automation Ideas

In the past, businesses had to resort to developing their own fragmented ETL testing automation solutions using Python, R, Java, or SQL. While effective, this approach comes with substantial resource and maintenance costs. Today, with the emergence of ETL automation testing frameworks, such as the one offered by Mozart Data, companies can simplify and optimize their ETL testing processes seamlessly. Furthermore, most databases in data warehouses adhere to the widely dominant Structured Query Language (SQL), making Mozart Data’s automation framework a powerful and efficient solution.

Mozart Data ETL Testing

Adhering to best practices, it is essential to test data after transformation and before loading it into your data warehouse to ensure it meets your requirements. Mozart’s modern data platform offers a transformation layer built on a user-friendly SQL editor, seamlessly integrated with the Snowflake data warehouse. With Mozart’s automated process of running SQL queries for ETL testing, you can trust that your data is reliable and meets the highest standards.


Our platform goes beyond piecemeal solutions, offering cutting-edge ETL testing automation ideas with significant advantages. With two types of data alerts, you stay informed when issues arise:


  • Notify-only: These alerts will run after a connector sync, transform, or snapshot. Notification is sent automatically.
  • Revert and notify: If a transform occurs, this will stop the transform, revert to its previous state, and send a notification.


Partner With Mozart Data for ETL Automation

At Mozart Data, our mission is to empower you to harness the full potential of your data, even without engineering or technical knowledge. 

When you’re ready to take the next step towards a modern data platform supporting your data warehousing and business intelligence goals, schedule a demo here or try us out for free here.