Why get an ETL tool in 2020?
The modern business uses so much data from so many sources that there’s no practical alternative to ETL for extracting and processing that data to make it available and useful for analytics and other applications. Connecting users directly to data sources forces them to do all the work to connect, process, and understand the raw data – a task that’s beyond most business users and is simply not feasible at enterprise scale. Using hand-coding to create an ETL process to consolidate the data, standardize data formats, and load it into a source system is too time-consuming and can result in a brittle bird’s nest of code on top of code. There are many benefits of ETL: the ability to extract data from multiple sources, and, now, the ability to load data into a cloud data warehouse and use the power and scale of the cloud to transform that data for analytics.
That means the question for any data-driven business today isn’t whether to use ETL tools. It’s how to use modern ETL tools to improve data management so your business users can get more value from your data sooner. Read on to find out the seven main benefits of ETL.
The 7 Biggest Benefits of Using an ETL Tool to Tame Data
One of the most important benefits of ETL is its ability to ensure that business users have fast access to large amounts of transformed and integrated data to inform their decision making. Because ETL tools perform most processing during data transformation and loading, most data is already ready for use by the time it’s loaded into the data store. When BI applications query the database, they don’t have to join records, standardize formatting and naming conventions, or even perform many calculations to generate a report – which means that they can deliver results significantly faster. An advanced ETL solution will even include performance-enhancing technologies like cluster awareness, massively parallel processing, and symmetric multi-processing that further boost data warehouse performance.
Provide a visual flow
Modern ETL applications feature a graphical user interface (GUI) that makes it easy for users to design ETL processes with minimal programming expertise. Instead of wrestling with SQL, Python or Bash scripts, stored procedures, and other technologies, all your users have to do is specify rules and use a drag-and-drop interface to map the flows of data in a process. Being able to see each step between source systems and the data warehouse also gives them greater understanding of the logic behind the data flow. These self-service tools also contain great collaboration tools, making it possible for more people in the organization to participate in developing and maintaining the data warehouse.
Leverage a an existing development framework
ETL tools are specifically designed for complex data integration tasks like moving data, populating a data warehouse, and integrating data from multiple source systems. They also provide metadata about the data they handle and help manage data governance tasks, which supports data quality processes and helps even novice teams build and extend data warehouses.
Provide operational resilience
ETL solutions provide the necessary functionality and standards for catching operational problems in the data warehouse before they create performance bottlenecks. They automate and monitor data flows, alerting the IT team to errors during transformation. By minimizing the human error inherent in hand-coded solutions, the ETL process makes data processing more efficient and reduces the likelihood of downstream data integrity issues.
Track data lineage and perform impact analysis
The best modern ETL solutions give users deep insight into the data catalog, allowing them to drill down into reports to see how each result was generated, what source systems the data came from, where the data was stored in the data warehouse, how recently it was refreshed, and how it was extracted and transformed. ETL also lets users determine how changes in the data schema might affect their reports, and how to make the necessary adjustments.
Enable advanced data profiling and cleansing
Business intelligence, machine learning, and other data-driven initiatives are only as good as the data that informs them. ETL tools support solid data management by letting you apply and maintain complex universal formatting standards and semantic consistency to all data sets as you move and integrate them. This helps all your teams understand each other’s needs and find the most relevant data based on their business context.
Handle Big Data
Modern ETL tools can combine very large data sets of both structured and unstructured data from disparate sources in a single mapping using Hadoop or similar connectors. They can also prepare very large data volumes that don’t need to be stored in data warehouses for use by data integration solutions.
Are You Ready to Start Taming Your Data?
ETL increases the speed and efficiency of extracting, transforming, and loading vast amounts of data into your data warehouse while ensuring that the quality of that data is as high as possible. That drives greater accessibility to data and faster, more reliable queries and reports – which in turn boosts the ROI of your investment in data warehousing
Matillion can help you realize the benefits of ETL at every stage of the process, from connecting to a wide range of data sources to ensuring the data in your data lake, warehouse, or other target system is ready for use. Take a more detailed look at how to deploy a modern ETL solution and help your business make better use of all its data in “What is ETL? The Ultimate Guide.”
See the benefits of Matillion ETL
Ready to see for yourself how Matillion ETL can help you tame your data by extracting, loading, and transforming it in the cloud? Get a demo.
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