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Data Preparation Doesn’t Have to be Burdensome: Data Transformation Made Easy With Cloud-Native ETL

When organizations begin to implement business intelligence (BI) solutions, they often are unaware of how much time it can take to parse data into a consistent format. Cloud data platforms, such as Snowflake, allow companies to pull together a tremendous amount of data from disparate systems throughout their organization into the cloud for analytics. But loading the data into a single location is not enough. You still have to put the data into a useful format, make the data analytics ready, so that it can bring value to the organization.

Rapid and effective analysis of data really matters in businesses today. Whether it’s improving sales funnels, understanding product use cases, catching inefficiencies on the production line, or improving employee hiring and retention, data analytics are what keeps today’s organizations competitive in a rapidly changing environment. Analytics solutions such as Tableau and Power BI and cloud data platforms such as Snowflake have made more extensive data analysis possible for organizations of all sizes, but they are just part of the puzzle. A complete data strategy must also include efficient data transformation to ensure that data coming in from disparate sources is cleansed, standardized and made analytics-ready as quickly as possible with minimal manual work steps or coding. Choosing the right cloud-native ETL (Extract-Transform-Load) tool sets companies up for rapid response to business challenges and opportunities.

 

Next Generation Cloud Data: Upgrade with Modern ETL

Many organizations that deploy a cloud data platform such as Snowflake are initially elated by the ease with which they are finally able to migrate data from disparate systems to a more powerful, extensible, and flexible cloud data environment. However, these organizations quickly realize that simply lifting and shifting the data to the cloud is not enough.

Every database, file system or application has its own way of organizing data and data tables. Dates and times may use different formats, the order of fields varies, and the way the tables connect to one another is unique to each system. Simple data loaders take the data as-is and place it in a cloud data platform without making changes to the underlying organization or format of the data. The next step is transforming the data – cleansing it to eliminate duplicates or missing fields, standardizing formats across different source systems, etc. – so that business analytics tools are able to compare apples to apples, so to speak. Something as simple as an email newsletter list might have different formats for customer contact info depending on where the data came from – intake from a web form versus contact info gathered at an event versus data from an order form. As a result, something as simple as following the customer journey from the top of the marketing funnel through to the sales organization at the bottom of the funnel requires standardization of the data formats before a BI or analytics program can make sense of the data.

 

Manual Data Transformation and Custom Code are Draining Valuable Resources

The first instinct of an organization and the data scientists may be to use manual cleansing and standardization techniques and customized code snippets to transform the data. It’s easy enough for a human to look at the data and understand how it needs to be fixed and standardized. As a result of that intuition, many companies start there, but end up with a huge library of Javascript code snippets that have been created by different engineers at different times. While creating custom code might be a viable short-term solution for a particular need, it’s not scalable and it’s extremely difficult to maintain, especially over time when there are inevitable changes to the staff.

The temptation is high to simply go with hand-coding, especially if the organization has just invested a large sum in the cloud data platform and wants to get analytics up and running as quickly as possible. When a data engineer ends up spending a significant amount of time coding and maintaining code, the cost is hidden in the salary budget, and it looks much smaller than the amount being spent on the cloud data platform. However, these “hidden” costs add up rapidly — manual coding can take as much as half of a data engineers’ time, and for new data engineers coming into the organization, ramping up on a high-code data environment can require a long time as they try to understand and learn to maintain the code written by their predecessors.

There are also additional costs to the business, such as slow response times to new requests and delays in making necessary changes to existing requirements. Whether it’s shifting customer preferences, supply chain or input changes, or upgrades in software systems, when the data transformation process is a bottleneck, response times are longer. Operational inefficiencies can waste ase much as 26% of employees’ time, and 25% of consumers said they have stopped doing business with an organization after just one bad interaction. The bottom line for your data environment doesn’t accurately display the costs associated with an inefficiency in operations, poor customer service, or missed market opportunities.

For all of these reasons, organizations need to take their ETL needs seriously. While the costs may not be visible, they are real.

 

Cloud and ETL Elasticity: Matillion ETL and Snowflake

The better-informed you are, the better decisions you can make. When it comes to business-data, having complete and up-to-date information is key to maintaining a competitive advantage.

Using Snowflake as a cloud-native data platform creates maximum elasticity in terms of the ability of Snowflake to scale up and down the amount of data being stored and processed in the platform. Matillion ETL leverages the computing power of an organization’s own cloud data platform, such as Snowflake, and uses exactly the amount of processing  needed at any given time to handle the data workloads that are running at that time. And since Matillion’s low code transformations require less processing, not only do the workloads run faster, they are also more cost effective.  In addition, because they run on separate virtual servers within a customer’s public cloud instance, no matter what the load on Matillion and Snowflake, the data transformation workloads do not have any impact on the performance of any other systems or workloads running simultaneously in an organization’s data stack.

The result of utilizing a cloud-based ETL solution like Matillion is that your business can grow and modernize its data stack and make wise decisions without the need to constantly invest in new data integration or transformation tools or create and maintain code. The ease of use of the no code/low code Matillion ETL means that your organization can integrate data even from smaller databases or niche systems, to complete the datasets on which your business depends. Just as importantly, decisions can be based closer to real time information, allowing your organization to be more agile and responsive to your customers.

 

What to Look for in a Data Transformation Solution

Traditional ETL systems were not built for the sheer volume, velocity, and variety of data that are coming at data scientists today. To stay competitive, modern ETL systems include a variety of features that speed up data transformation. Cloud-native ETL solutions such as Matillion speed up the data transformation tasks through:

  • Data source connectors: Matillion includes more than 100 pre-built connectors from data sources such as marketing and sales, accounting, ERP and other solutions. For most data transformation jobs, the built-in connectors suffice. In addition, Matillion comes with a drag and drop connector builder that makes it easy to build custom connectors to most source systems.
  • Built-in workflows: Based on years of experience with a variety of organizations, Matillion has built in workflows for many of the most common data collection and transformation tasks.
  • WYSIWYG drag-and-drop workflow interface: Creating custom workflows for data transformation is a cinch with drag-and-drop, no-code interfaces for specialized and new scenarios that data scientists want to create.
  • No-code/low-code customizations: There’s no compromise in terms of the types of customizations that Matillion offers. Low-code capabilities categorize and associate the code with the integrations and workflows that are easy to use, not just for the initial creator of the customization, but for new staff members who may need to take over from another engineer.
  • Security: Matillion does not store any data or workflow in the system. Instead, all of the data, connectors, workflows, and customizations are stored in the company’s cloud data platform, such as Snowflake. Matillion accesses the data in the cloud and leaves it stored behind the company’s security systems, ensuring that its cloud-native ETL does not add any attack vectors and does not require any additional security procedures by the IT staff.
  • Pay-for-use: As a cloud-native ETL solution, Matillion deploys a pay-for-use model where organizations pay only for the data processing that they require. There are no monthly retainers or lock-ins, so the solution is appropriate and affordable for organizations of any size.
  • Full support 24/7: If anything goes wrong, the service department is able to troubleshoot 27/7.

When choosing a modern ETL solution for their data strategy, organizations should consider all of the above factors when comparing cloud-native ETL solutions for cloud data integration and transformation.

If you’re using Snowflake, bringing in the Matillion cloud-native ETL completes the cycle so that you can make your data analytics-ready and consumable for your BI tools faster than ever before. Using a no-code/low-code ETL solution allows companies to glean actionable insights and respond rapidly to changing business conditions. Schedule a demo to see Snowflake and Matillion in action – from fast data extraction through efficient, no-hassle data transformation.