Here are some quick final thoughts about ETL and ELT:ĮTL is outdated. You can think of Hadoop as “a sandbox for a big data environment” in which your analysts can play around instead of treating it as a straight-up replacement for a data warehouse. In other cases, you can load highly unstructured data, such as tweets for sentiment analysis, that don’t require extensive upfront transformations. Hence, poor quality data or data that requires substantial integration shouldn’t be loaded into Hadoop, unless you have a team of highly skilled programmers to write custom codes for complex data transformations.ĭata sets loaded into Hadoop during the ELT process can be relatively simple yet massive in volume, such as log files and sensor data. The ETL process feeds traditional warehouses directly, while in ELT, data transformations occur in Hadoop, which then feeds the data warehouses. With Hadoop integration, large data sets that used to be circulated around the cloud and processed can now be transformed in the same location, i.e, within Hadoop.ĮLT is a good option if you’re moving to a data warehousing structure for supporting big data initiatives using Hadoop or a NoSQL analytical DBMS. Previously, large data sets were divided into smaller ones, processed and transformed remotely, and then sent to the data warehouses. Tools such as Apache Hadoop have renewed the interest of businesses in ELT. Hadoop and advanced data integration tools enable ELT This means that if your IT department is short on Java programmers to perform custom transformations, ELT may not be right for you.ĭespite these challenges, should you move to ELT? Are there any advantages in doing so? To answer these questions, we’ll take a closer look at the characteristics of the target systems used in the ELT process. Also, transformations in Hadoop are written by Java programmers, so you might need them in your IT team for maintenance purposes. One of the immediate consequences of this aspect is that in ELT you lose the neat visual interface and data preparation/cleaning features that ETL tools provide. In contrast, with ELT, the staging area is within the data warehouse, and the database engine powering the database management system performs the transformations. It sits between the source and the target system, and data transformations are performed here. In ETL, the staging area is within the ETL tool, be it proprietary or custom-built. However, with the evolution of underlying data warehousing storage and processing technologies such as Apache Hadoop, it has become possible to accomplish these transformations within the target system after loading the data, which is the process followed in ELT.īoth ETL and ELT involve staging areas. Precalculation of intermediate aggregatesįor traditional data warehouses, these transformations are performed before loading data into the target system, typically a relational data warehouse. Recombining of columns from different tables and databases This means that data must go through a series of transformations, such as: OLAP tools and structured query language (SQL) queries depend on the standardization of dimensions across data sets to deliver aggregate results. In this step, ETL and ELT differ in two major aspects:ĮTL tools help integrate data to meet the requirements of traditional data warehouses that are powered by online analytical processing (OLAP) data cubes and/or relational database management system (RDBMS) technologies. ETL implies for data warehouse integration with this pizza analogy:Įverything hinges on the “T” in ETL and ELTĭata transformation is the most complex step in the ETL and ELT processes. The image below explains the different business scenarios suitable for the ETL and ELT data integration methods.įind more about what ELT vs. The business use cases for the data warehouse The design approach to data warehouse architecture To understand their differences, you also have to consider: That said, the difference between these two processes isn’t just confined to the order in which data is integrated. In ELT, after extraction, data is first loaded in the target database and then transformed data transformation happens within the target database. In ETL, data is extracted from disparate sources such as ERP and CRM systems, transformed (calculations are applied, raw data is changed into the required format/type, etc.), and then uploaded to the data warehouse, also called the target database. What is the difference between ETL and ELT?
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