Getting data from its raw format into one that makes sense for business users is a huge challenge for most data-driven organizations. Most business users don’t understand the complexities of data models or writing code; they simply need the insights gained from analyzing data. Without a reliable means of taking information from data sources and turning it into a user-friendly format, data has little meaning for those who need it most.
ETL tools solve this issue by gathering data from sources, changing it into understandable formats, and putting the transformed data in repositories for specific business analytics uses. We’ll explain exactly what ETL tools are, why they’re important, the benefits of using them, options for these tools, and how to select the right one for an increasingly cloud-first data landscape to make working with data easier.
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What is an ETL tool?
Extract, Transform and Load (ETL)) is the process used to turn raw data into information that can be used for actionable business intelligence (BI). An ETL tool is an instrument that automates this process by providing three essential functions:
- Extraction of data from underlying data sources
- Data transformation in order to meet the data model of enterprise repositories like data warehouses
- Data loading into target destination
ETL tool options
Although there are multiple ETL tools, not all are built for the modern data environment. Organizations need tools that are flexible and quick enough for the pace of business today. Ideally, they should also support a variety of use cases. Some of the ETL tools used throughout the data landscape today include:
- Incumbent or legacy ETL tools: These tools still provide core data integration functionality, but are slower, more brittle, and less flexible than contemporary options. Many of these tools are code intensive and lack the automation (especially for real-time deployments) compared to other selections.
- Open-source ETL tools: Open source ETL tools are a lot more adaptable than legacy tools are. They work on data of varying structures and formats — legacy tools basically work only on structured data. The open-source nature of these tools make them faster than most legacy tools.
- Cloud-based ETL tools: Cloud-based ETL tools make data readily available, and are flexible enough to account for the different structures associated with big data. Because of this flexilibility, cloud-based ETL tools are more effective than on-premises options for dealing with hybrid cloud data source.
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Why you need an ETL tool
There are a number of reasons why organizations need ETL tools for the demands of the modern data landscape. One of the main reasons is these tools automate and streamline data pipeline processes. Reputable ETL tools reduce the time spent on manual processes such as writing code and mapping source data to target systems. They make these tasks easily repeatable, cost-effective, and faster.
Additionally, ETL tools are the best means of handling complex data management tasks. The influx of artificial intelligence and machine learning means organizations are using data from a larger number and variety of data sources than ever before. Widespread adoption of the cloud means data sources are more distributed than they were, while real-time data that comes from the Internet of Things means that the speed of analytics must increase. Cloud ETL tools can meet all these demands so organizations aren’t struggling to keep up.
Finally, ETL tools are a must for data governance demands. Regulations like GDPR hold organizations accountable for ensuring digital privacy. Using ETL tools with standardized, repeatable data governance processes helps to ensure data governance needs are met to fulfill this and other regulations. ETL tools are also key for implementing data quality so organizations have data that’s both trustworthy and accurate. These instruments facilitate data quality and data governance at enterprise scale.
The benefits of using an ETL tool
ETL tools help organizations manage their data in several ways. In particular, they excel at providing the following benefits.
- Scalability: Good ETL tools can scale up and down to accommodate the needs of business users. In some instances, those needs center on huge batch jobs of big datasets. In others, it could be smaller datasets for exploration.
- Real-time: ETL tools are excellent for real-time operations with data. Competitive tools enable users to specify the rate at which jobs are performed, which can be every couple of seconds, every five minutes, or any other time frame to handle low-latent ETL needs.
- Automation: Although some of the automation benefits of ETL tools pertain to their real-time capabilities, they also apply to less frequent tasks like nightly batch jobs. With these tools, the ETL process needs to be set up once and then organizations can reuse it at will.
- Governance: Credible ETL tools have governance features that are highly important for ensuring data integrity and accuracy. Some of the more important capabilities include data lineage for regulatory compliance (even down to the transformation level), metadata management, and lifecycle management.
Selecting the right ETL tool for you
Smart organizations will consider a variety of factors before deciding which ETL tool is best. Some of the most relevant ones include:
- Use case: Ultimately, use case is one of the more determinant considerations in ETL tool selection. If organizations are simply tallying up weekly or monthly sales figures, for example, older ETL approaches may suffice. However, when there are a variety of different use cases, or ones involving distributed cloud options, more modern approaches are beneficial.
- Capabilities: ETL tools should be flexible to read and write data regardless of where it is, whether on-premises or in the cloud. They should also include specific functions for data quality like de-duplications, as well as for collaborating with others to reuse processes. Good ETL tools also let you quickly switch providers, like ingesting data from AWS and Microsoft Azure without lengthy delays.
- Data sources: The type of data sources involved is a key consideration when selecting ETL tools. Some organizations may only need to work with simple structured data; others may need to account for high-dimensionality, structured and unstructured data. Not every type of tool can quickly accommodate the demands of the latter.
- Integration: The key integration factors for determining which ETL tool works best for a company are the scope and the frequency of the integration efforts. More demanding jobs requiring several integrations each day, or ones that involve many decentralized sources, require modern ETL approaches.
- Business user: The data fluency of the business user is important for selecting an ETL tool. Most business users aren’t well versed in the particulars of transforming data, and may need a tool that automates this process. Additionally, organizations should consider how long the business can wait before its data is available.
- Budget: Budget is always an important consideration in any tool selection. ETL options that require a lot of manual coding and data mapping have the added costs of continually paying employees to perform these functions. Certain cloud ETL options that also deliver ELT can reduce costs by transforming the data inside the data store, using the resources of the repository.
- Business goals: Business needs are potentially the most critical consideration when selecting ETL tools. It’s important to get the business the instruments it needs to perform well in terms of speed, effectiveness, and flexibility for its data integration needs.
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Getting started with a cloud-based ETL tool
ETL tools are the instruments that do the heavy lifting of data integration for many applications like BI. They’re necessary for accommodating the scale and variety of the decentralized data landscape. Cloud-based ETL tools are responsible for automating many of the processes in this analytics requisite at a speed equal to the contemporary pace of business.
Talend Data Fabric specializes in incorporating ETL as part of a larger framework for managing data. As a comprehensive suite of apps for collecting, transforming, sharing and governing data, Talend Data Fabric replete with many mechanisms for instituting data quality and data governance. These functions are key for ensuring that the resulting data from ETL is trustworthy, clean, complete, and follows data governance standards.
Try Talend Data Fabric to automate your ETL process today and gain data you can trust at the speed of your business.