Etl Tools In Business Intelligence – ETL and ELT are the most widely used methods for delivering data from one or many sources to a centralized system for easy access and analysis. Both consist of the
Stages. The distinction is based on the sequence of events. While you might think that a slight change in staging order would have no impact, it makes a world of difference to the integration flow.
Etl Tools In Business Intelligence
In this post, we’ll dive deep into explaining ETL vs ELT processes and compare them to essential criteria so you can decide which is the best fit for your data pipeline.
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As we mentioned before, ETL and ELT are the two ways to integrate data into a single place. The primary difference lies in
It arrives at a target repository (eg an enterprise data warehouse) while ELT enables data to be transformed
It is loaded into a target system (cloud data warehouses or data lakes). Why does it matter so much? Let’s find out.
. It is the process of collecting raw data from disparate sources, transferring it to a staging database for conversion, and loading prepared data into a unified destination system.
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ETL tools are used for data integration to meet the requirements of relational database management systems and/or traditional data warehouses that support OLAP (online analytical processing). OLAP tools and structured query language (SQL) queries require data sets to be structured and standardized through a series of transformations that occur before data ends up in a warehouse.
The approach originated in the 1970s when companies started using multiple data repositories for working with different types of business information. As the number of disparate databases grew, so did the need to consolidate all that data into a centralized system. ETL came to meet that need and became the standard data integration method. From the late 1980s, when data warehouses appeared, and until the mid-2000s, ETL was the main method used to create data warehouses to support business intelligence (BI).
As data continues to grow in volumes and types, the use of ETL becomes not only rather ineffective, but also more expensive and time-consuming. And ELT comes to the rescue.
With the exploding number of data sources and increasing need to process massive data sets for business intelligence purposes and big data analytics, ELT, the alternative to the traditional data integration method, is gaining popularity.
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. ELT basically reverses the last two stages of the ETL process, meaning that after being extracted from databases, data is loaded straight into a central repository where all transformations take place. The staging database is absent.
The approach is possible thanks to the modern technologies that make it possible to store and process large volumes of data in any format. This includes Apache Hadoop, an open-source software that was initially created to continuously ingest data from different sources, regardless of type. Cloud data warehouses such as Snowflake, Redshift and BigQuery also support ELT, as they separate storage and compute resources and are highly scalable.
The data flow of both ETL and ELT relies on three core stages. Despite the identical names, the stages of each approach differ not only in the order in which they occur, but also in the ways in which they are performed.
The data journey always starts with extracting and copying it from a pool of sources – ERP and CRM systems, SQL and NoSQL databases, SaaS applications, web pages, flat files, emails, mobile applications, etc. The initial phase can be quite complicated due to the complexities of each source system.
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With the ETL method, users must plan ahead which data items should be extracted for further transformation and loading. ELT, on the other hand, makes it possible to extract and load all the data instantly. Users can decide which data to transform and analyze later.
The transformation phase involves a variety of activities aimed at preparing data by changing it to fit the parameters of another system or the desired result.
In ETL, all these operations take place outside the destination system in a staging area. There are data engineers responsible for implementing these processes. For example, Online Analytical Processing (OLAP) data warehouses only allow relational data structures so the data must be transformed into the SQL-readable format beforehand. Any conversions can only happen once, which makes ETL quite inflexible. If a new type of analysis is needed to apply to the already transformed data, you can end up changing the entire data pipeline from scratch.
The ELT method is flexible and transformation friendly as data goes directly to a data warehouse, data lake or data lake house where it can be validated, structured and transformed in different ways at any time. Moreover, raw data can undergo numerous transformations as it is stored indefinitely. Since everything happens within a target system, data analysts can assist data engineers in performing transformations using SQL for this purpose.
Etl Process And Tools In Data Warehouse
This stage applies to loading data into a target data storage system so that users can access it. The ETL process flow implies importing previously extracted and already prepared data from a staging database into a target data warehouse or database. This is performed by physically inserting separate records as new rows into the warehouse table using SQL commands or using a massive bulk load scenario.
ELT, in turn, delivers the bulk raw data directly to the target repository, skipping an intermediate level. This greatly cuts the withdrawal-to-delivery cycle. As with extraction, data can be either fully or partially loaded.
To help you understand the benefits and limitations of both approaches to data integration, we’ve singled out the most important criteria against which ETL and ELT will be compared.
ELT is a relatively new methodology, which means that there are fewer best practices and less expertise available. Such tools and systems are still in their infancy. Specialists, who know the ELT process, are harder to find.
What Is Etl (extract, Transform, Load)?
The ETL practice, on the other hand, is quite mature. Since it has been around for a while, there are quite a few properly developed tools, experienced specialists and best practices to rely on.
Along with structured data, ELT allows for the processing of large amounts of non-relational and unstructured data, which is necessary for efficient big data analytics and BI.
In the event that all the source data comes from relational databases or when it needs to be thoroughly cleaned before being loaded into a target system, ETL is often chosen over ELT.
Flexible and scalable, ELT surpasses its older sibling in terms of its ability to ingest massive sets of different data types.
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ETL is applied when working with OLAP data warehouses, legacy systems and relational databases. It does not provide data lake support.
Not only is ELT generally cost-effective versus high-performance on-premises solutions, but it also offers a lower cost of entry. Many cloud providers offer flexible pay-as-you-go pricing plans.
Architectures using traditional ETL processes can be expensive due to the initial investment in hardware and costs associated with the power of the transformation engine. At the same time, modern cloud ETL services also offer flexible pricing plans based on usage requirements.
ELT is a cloud-based solution with automated functions that require little or no maintenance. All data is ready and can be transformed piece by piece for analytical purposes.
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ETL processes require higher maintenance when it comes to on-site solutions with physical servers. Speaking of cloud ETL solutions with automated processes, things are much the same as with ELT: it is not maintenance intensive.
Unlike ETL, ELT reduces the load times due to the built-in processing capabilities of cloud solutions that allow data to be loaded in its raw formats without prior transformations.
With ETL, the process of data loading is slower because of the need to transform data on a separate processing server before ingesting it into a destination system.
With ETL, transformations are performed on a separate server and are significantly slower, especially with regard to large volumes of data.
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In ELT, a target system is responsible for transformations. Due to the separate storage and computation, data can be stored in its original formats and converted on demand. The size of data does not affect the speed.
With ELT, data is uploaded “as is” without any reduction or encryption done beforehand, which can make data vulnerable to hacking and violate some compliance standards.
ETL makes it possible to redact, encrypt or remove sensitive data before it reaches a data warehouse. As such, it is easier for companies to protect data and adhere to different compliance standards, including HIPAA, CCPA or GDPR.
Due to ELT immaturity, specialists with expertise in this methodology are hard to find. Kafka, Hevo Data, Talend and some other software provide comprehensive ELT capabilities along with ETL.
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Performing processes such as data acquisition, export, transformation and migration requires skilled ETL specialists. Fortunately, finding the talent is easier. Informatica, Cognos and Oracle are some examples of traditional ETL tools.
In conclusion, both processes have their advantages and limitations. To decide on the winner of the rivalry (if there is one), let’s look through the possible use cases of ETL and ELT.
Cloud data warehouses have opened new horizons for data integration, but the choice between ETL and ELT depends on the needs of a company
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