What Is ETL? ETL is an acronym for "Extract, Transform, and Load." You must have heard the word "ETL" about data, data warehousing, and analytics if you're reading this. To merge data from various sources into a unified database, you generally rely on a single ETL solution. It is crucial in making data suitable for reporting, analytics, and even modern applications like machine learning and artificial intelligence. However, the nature of ETL, the data it processes, and where the ETL process takes place have all changed dramatically in the last decade, making the correct ETL software more important than ever.
Here are some examples of ETL tools:
Cloud-Native ETL Tools
Once carried out on-site, ETL tasks have migrated to the cloud. Numerous cloud-native ETL tools have surfaced, enabling direct data extraction and loading into a cloud data warehouse. They can then convert data utilizing the cloud's power and scalability, which is important when dealing with Big Data. These ETL solutions may be implemented directly into your cloud infrastructure or offered as a SaaS in the cloud.
Open Source ETL Tools
Many companies find open-source ETL tools a cost-effective alternative to commercially packaged ETL systems. While some open-source solutions only serve one part of ETL, such as data extraction, others do many functions. Apache Airflow, Apache Kafka, and Apache NiFi are examples of popular open-source software. One disadvantage of open-source ETL projects is that they need to be built to manage the data complexity that modern businesses confront and may need more support for complicated data transformation and desirable features like change data capture (CDC). Furthermore, finding help for open source best ETL tools might be more difficult than other programs with comprehensive support teams.
Real-Time ETL Tools
We increasingly require real-time access to data from various sources. You want to see modifications and comments only after you collaborate in Google Docs. With today's time-sensitive needs, waiting even a few hours to view transactions and transfers is unacceptable if you work in finance. Real-time demand necessitates handling data in real-time, rather than in batches, using a distributed paradigm and streaming capabilities.
To remain competitive, a company must accomplish three things:
These are three equally vital legs of a forward-thinking business intelligence strategy for today's organizations. With them, it will be extremely easy for a company to remain relevant over the next five years.
Data Integration
What is ETL in data? Data flows from various sources like forms, phones, and sales tools. To make sense of it all, you need data integration. This process takes data from different places, transforms it for consistency, and makes it usable. For instance, it can clean up messy address entries, so you don't end up with 'UT,' 'Utah,' and 'Utha' in your user database."
Data Trustworthiness
Data you can use is good; data you can trust is priceless. When building your data system, focus on data integrity (accuracy and error-free data) and governance (rules and policies). Integration tools should work with integrity solutions to prevent bad data. Governance establishes who can do what with data, setting clear rules and access protocols. In regulated industries like healthcare, extra safeguards are vital.
Storage
As your data needs to grow, you'll need a central data repository. Choose between data lakes (suitable for structured and unstructured data) and data warehouses (for structured data, ideal for reporting). Many use both, feeding a company-wide data lake into team-specific data warehouses.
Cloud and Multi-Cloud
The cloud is essential for flexibility and remote work, especially post-COVID. Multi-cloud, deploying across platforms, offers even more benefits for growing businesses:
Choose adaptable solutions for a strong data infrastructure that grows with your company's evolving needs.
Creating a reliable and streamlined data infrastructure can be complex, but it's important to avoid common pitfalls. One critical aspect is minimizing technical debt, ensuring your data system is efficient, manageable, and cost-effective. Unfortunately, many companies employ suboptimal practices like patchwork ETL pipelines and overreliance on Excel for data transformations.
Choosing how you bring in your data is a critical decision. It will impact the workload and maintenance of your future engineers.
You have options like ETL and ELT:
Python frameworks like Airflow and Luigi give flexibility with built-in features for ETL. Streaming tools like Kafka, Kinesis, and Rockset enable real-time data processing, which is vital when immediate information is needed, such as monitoring multiple factories worldwide.
Many people wonder why we still need ETL now that we're in the cloud. Is it still important? The answer is "Yes." Absolutely." ETL provides various business benefits beyond extracting, cleaning, conforming, and delivering data from Point A (source) to Point B (destination). ETL is required in the cloud for the same reasons as in a traditional data warehouse. Your data must still be brought to a central repository, although from more sources than ever before, in organized and semi-structured formats. These massive data repositories must be translated into forms suitable for examination.
ETL prepares data for quick access and, as a result, quick insight. Data must be collected and processed in business intelligence tools, such as data visualization ETL software, or it would be useless in the cloud, just as it would be in a data center's raw form.