Modern enterprises gather data from many disparate sources, including social media, websites, customer databases (CRM systems, sales records etc), customer support systems, and HR software.
Merely collecting lots of data isn't useful in itself—it's the insights you get from such data that can drive more informed business decisions. This post aims to teach you about the bedrock processes required for obtaining insights from your data—ETL and ELT.
We'll begin by discussing data integration, before moving on to two vital forms of data integration—ETL and ELT. When you're finished reading, you'll fully understand what ETL and ELT are, and which process is better suited to your business in terms of getting actionable insights from different data sources.
To make informed analyses of your data, you need a method of consolidating data into a unified format for use with BI and analytics tools. Data integration refers to processes used to combine data from information silos into a unified view that provides meaningful and actionable information.
Data integration is particularly important in data warehouses, which are centralized repositories of data accumulated from many sources of enterprise data. Companies use a data warehouse for reporting and data analysis—the problem is changing data which is structured for transactional purposes to be used for reporting and analysis purposes.
Data warehouses are either on-premise or based in the cloud, with the latter becoming increasingly popular thanks to the convenience, lower cost, and scalability of cloud infrastructure. Integrating data into data warehouses for BI is typically done using ETL or ELT processes, both of which ensure data is clean, manageable, and ready to analyze.
ETL (Extract, Transform, Load) is a form of data integration that pulls data from different sources, manipulates the data into a consolidated format (normally inside a staging database), and finally loads the data to target systems, which are often data warehouses, such as Amazon Redshift.
ETL is quite an old technology, having initially gained popularity in the 1970s. ETL tools in the form of special software are often used to ETL data from sources to the data warehouse because it is much easier to use a tool than code the ETL process by hand.
ETL tools are optimized for the ETL process, with connectors to common enterprise data sources like databases, flat files, mainframe systems, and xmls.
Commercially available ETL tools include Ab Initio, IBM InfoSphere DataStage, and Informatica. Open source ETL tools such as Apatar, CloverETL, and Pentaho provide free alternatives that can integrate with many data sources.
Commercial ETL tools generally have better support, which is important with a complex process such as data integration. Open source tools, of course, are more cost-effective and are associated with lower business risk because of the minimal investment required—time is the only cost.
Despite ETL being an old technology, it has several benefits as a form of data integration, namely:
ETL provides a structured way to move and integrate data into centralized repositories for analysis.
Most ETL tools include operations such as filtering, reformatting, sorting, joining, merging, and aggregating data. Such operations are difficult to code manually, making ETL convenient.
ETL enhances business intelligence because it gives access to data from disparate sources within an enterprise. ETL helps companies become data-driven.
ETL processes can be used as a platform to build data streaming pipelines for real-time data analysis with the help of projects such as Apache Kafka. The availability of such up-to-date data changes organizational environments from reactive and based on aged data, to an environment of continuous learning and refining of business decisions based on real-time analytics.
ETL is useful for when data integration must be performed repeatedly and regularly, such as in hourly or daily intervals.
ELT (Extract, Load, Transform) is a variation on ETL in which raw data is extracted from sources and immediately loaded into target systems, such as data warehouses. Data is transformed inside the target system—this method can be used to leverage the storage capacity and computational power of cloud-based data warehouses by negating the need to transform raw data before loading to the warehouse.
ETL vs. ELT: Comparison
Let's take a look now at some of the main ways ETL and ELT approaches to data integration differ.
Load Time—it takes significantly longer to get data from source systems to the target system with ETL because business rules that make data more understandable are applied upon loading. With ELT, you simply extract and load raw data, making data almost instantly available.
Transformation Time—significant waiting times are needed to wait on transformations in ETL, particularly with large volumes of data. In ELT, transformation is carried out on demand using the target system's computational power.
Complexity—several ETL tools have an easy to use GUI that simplifies the process. ELT requires in-depth knowledge of BI tools, masses of raw data, and database design to effectively transform data as it is queried.
Data Warehouse Support—ETL is better suited to legacy on-premise warehouses and structured data. ELT is tailored for the cloud infrastructure and its effortlessly scalable storage.
Maintenance—ETL requires significant maintenance because you must go through the time-consuming process each time you want to update data in the data warehouse. With ELT, data is always available in near real-time.
The comparison shows that ELT is the future of data warehousing because it offers many advantages over ETL, which is an older, slower form of data integration. ELT delivers better agility and less maintenance, making it a cost-effective way for businesses of all sizes to leverage the current cloud-oriented data warehouse services.
Data volume has grown for organizations of all sizes over the last decade, and ETL tools simply can't handle the integration of all this data into a repository for analysis.
All businesses can utilize “Big Data” from within the company and outside, regardless of company size, necessitating a process that integrates this data for reporting and analysis without long waiting times—ELT is that process.