What is ETL: Benefits, Challenges & Recent Advances
Gathering business intelligence necessary for growth requires a lot of time, effort, and the right data analytics tools. However, many data reporting tools don’t have enough capacity to store all the data from various analyses. You often end up with incomplete or inaccurate data, which can disrupt your business operations and negatively affect your revenue.
An excellent solution can be a business intelligence tool powered by ETL or a dedicated ETL tool. Let’s see what ETL is and how it can benefit your business before exploring some of its challenges.
What Is ETL and How Does It Work?
ETL stands for Extract, Transform, and Load, which are the primary steps in data integration and data migration.
The ETL process can help you build a data warehouse, a data lake, or a data hub by synthesizing silos of data from multiple sources, ensuring you create an accurate, reliable, and streamlined data flow.
Let’s go over each step to help you understand it better.
Data extraction is the process of pulling data from one or more sources, such as analytics tools, data warehouses, CRM systems, marketing and sales apps, cloud environments, and many other databases.
It’s the process of extracting structured and unstructured data and storing it in a centralized location.
During the transformation phase, the extracted data is analyzed for quality. That means ensuring there are no inconsistencies, errors, missing values, or duplicate records.
If there are any anomalies, the ETL tool flags them, removes any unusable data, and discards redundant information.
After those analyses, cleansing, deduplication, and verification processes, the data is standardized, sorted, and ready for the loading phase.
The transformation step is the most important because it improves data integrity and ensures your data is useful, high-quality, and accurate.
The loading process represents importing your transformed data into your data warehouse or another location that works for your business.
You can load all of it at once, which is known as full loading, or do it in batches or real-time, known as incremental loading.
The latter might be a better choice, as it’s easier to manage and doesn’t put too much strain on your data warehouse or data lake. What’s more, it also prevents data duplication by checking the database before making new records of any incoming data.
What Are the Benefits of ETL?
By collecting large quantities of data from multiple sources, ETL can help you turn data into business intelligence. It can help you drive invaluable insights from it and uncover new growth opportunities.
It does so by creating a single point-of-view so that you can make sense of the data easily. It also lets you put new data sets next to the old ones to give you historical context.
As it automates the entire process, ETL saves you a great deal of time and helps you reduce costs. Instead of spending time manually extracting data or using low-capacity analytics and reporting tools, you can focus on your core competencies while your ETL solution does all the legwork.
One of the greatest benefits of ETL is ensuring data governance, that is, data usability, consistency, availability, integrity, and security.
With data governance comes data democracy as well. That means making your corporate data accessible to all team members who need it to conduct the proper analysis necessary for driving insights and building business intelligence.
What Are Some of the Biggest ETL Challenges?
As much as ETL can benefit your business, it comes with particular challenges that you shouldn’t overlook. They can lead to inefficiencies, performance problems, and operational downtime.
One of the most notable ETL challenges has to do with the sheer amount of available data. When tackling massive data sets, it’s not uncommon for ETL tools to make mistakes.
You may end up with some data loss, corrupted or irrelevant data because some processes in the transformation phase may not have performed correctly. You may also end up dealing with many bottlenecks because of insufficient memory or CPU.
Disparate data sources are another big challenge with ETL. Not every source database and destination system are aligned, meaning they don’t have the same type of coded mappings.
In such cases, you may need to conduct a host of different data transformations. That defeats the whole purpose of ETL.
It could also lead to redundant or duplicate data and compromise data integrity and quality. You could have trouble normalizing your data warehouse or data lake, thus experience downtime and performance issues.
It’s not always easy to get the transformation process right. If you’re dealing with poorly-coded mappings, you’re bound to experience many issues, such as missing values and unusable data.
Recent Advances in the ETL Space
ETL processes are increasingly moving to the cloud and relying on IoT and big data. They can handle real-time data streaming and efficiently and effectively parse data for business intelligence and actionable insights.
Speaking of which, many organizations are increasingly relying on event-driven architecture and distributed messaging and streaming systems for better integration and data flow.
Data lakes are becoming the go-to data repositories for storing raw and transformed data in any format. But with data lakes, the process is changed to ELT – Extract, Load, Transform. That solves many ETL challenges, such as eliminating discrepancies between existing and incoming data sets.
Machine learning and AI have enabled smart data integration, which might make the ETL processes obsolete in the near future.
How Pandio Fits Within the ETL Ecosystem
Pandio offers a fully-managed distributed messaging system built on Apache Pulsar that solves challenges in AI, machine learning, and big data.
It can help you migrate data warehouses, create data lakes, move to the cloud, enable AI and ML, connect to multiple systems, break down data silos, and much more.
It’s cost-effective, reduces complexity, supercharges performance with optimal latency, enables seamless integrations, enhances security, and provides regular upgrades and patches. It eliminates operational burdens and helps you focus on what matters.
Pandio and Apache Pulsar as a Service support your AI-powered and data-driven future.