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ETL programming is a process where you can leverage automation to combine and merge data from different sources into a single data source.
The goal of ETL programming is to automate the transfer of data from a source into a destination, such as a data warehouse like Snowflake, for example.
The ETL process incorporates a mix of machine learning and data analysis. ETL programming can organize and declutter your data into a single helpful source.
Different uses of the ETL process help a company in the following ways:
ETL is crucial for any organization that relies on data storage and warehousing.
ETL tools play a important role in reading, collecting, and distributing huge chunks of raw data. The collection takes place from many sources and reaches a single platform.
Later the data can be used for different practical purposes. For example, data engineering is a common ETL use case.
ETL processes include sorting, filtering, merging, and aggregation. After the final stage and arrangement, a graphical representation of data can be visualized.
Building data pipelines are another essential use case for ETL.
Extract: The extraction step is about taking out the data from the source.
Transform: The transformation step is about changing and combining the data as needed.
Load: The loading step is about keeping the whole data in a single database.
ETL process allows you to refine, merge and clean your data and keep them in the same data lake.
The extraction is the first step that comes in place in the ETL process.
Before combining and loading data, data engineers must capture raw data from different sources.
During extraction, the ETL process takes data from many sources and brings the same to the staging area.
During the data transformation phase, the raw data in the staging area gets processed.
Later, the extracted data changes into something meaningful by cleaning, duplication, validating, and checking the data.
It can do tasks like calculating, making summaries, and even translations.
After extracting and transforming, the next step is load. The load process will send the final data to the target location.
At first, the process sends the data to the last site. After that, there are gradual inputs of data to the warehouse.
Most organizations have automation for the ETL process. There is a setting that allows the ETL process to work when the load and traffic on systems are less. There is hardly any pause in the data pipeline.
There is little difference between ETL and ELT. The only thing that brings a change in these two processes is the order of experience.
The data gets extracted from various sources. The next step is direct loading (skips transformation). Hence, there is no waiting at the staging area for conversion.
The ELT process is in use when we are dealing with high-volume datasets. Also, when dealing with unstructured information, it comes into play. ELT is better at managing more essential data when you do not need much planning.
ETL needs more planning and a kickstart. There are specific points that you have to identify and notice. Even after completing work, you must keep some rules for the transformation.
SQL stands for Structured Query Language. It is a language used for the programming and management of data. People also use it for the process of relational data stream managing purposes.
SQL is a language to manage the query data in a relational database. Do you know that you can also use SQL for the ETL process? You will not need any ETL tool to carry out the ETL process.
Yes, you can do ETL by using SQL or Python.
Many analysts and engineers use these languages to build their data pipeline.
Creating your pipeline gives you more command and control. Also, one can control and change any aspect of the channel as per need. It is one of the prominent reasons people use these languages for the ETL process.
Using SQL or Python will also demand more effort and time from you. So, yes, you can create the data pipeline, but it needs more maintenance.
You will see this in interactive ETL solutions. ETL jobs have a use of this process.
There are many advantages to using ETL tools, such as:
The most significant advantage you get from using ETL tools is the ease of operation.
ETL tools are easy to use. You can carry out the whole process with automation.
Also, you do not have to stress about manually controlling data. Everything will go in sync for you. ETL tools can check unstructured data and load transformed data to the target system.
ETL tools save you massive time.
ETL tools make data workflows easy with the help of a visual interface.
ETL automation allows data to sync without manual loading.
The transformation process on the staging area is completed easily with the help of an ETL tool.
You do not need to manually interfere with the process of data cleansing or loading into the data warehouse.
ETL tools take care of data migration on their own, enabling you to reduce engineering headcount and save money.
Automation can course-correct the errors that commonly occur in manual work.
With an ETL tool, data migration can often happen with just one click.
ETL process helps you to handle big data in a good way. Read the guide to big data on Portable to learn more.
ETL tools can load many data sources at once.
ETL tools can extract the data from several individual platforms and load them into the target destination.
Also, ETL processes can help you with the loading process with convenience. Storing metadata with ETL is also an advantage.
Your CPU processor can get a heavy load when carrying out the ETL process. ETL process demands high-scale and high-volume extractions. Because of this, there are times you need to shift to low-scale operations. Hence, it is one limitation that comes ahead of engineers.
You need to process the rules and terms in advance before the operation. Providing the rules in advance has one major challenge. It will offer you lesser flexibility. You will need to change the practices again and stop the operation at the change hour.
One of the challenges to using the ETL tools comes with experience and knowledge. To use the tool, one must be a data developer or analyst. Hence, not everyone can use these tools. The need for expertise stops many from using this tool for their ETL process.
When you seek a real-time or fast response for data access, it can challenge the ETL process. One might need months to complete the process and complete things. Also, it can take your time when you want to catch up with changing needs.
There are tons of ETL tools available today. Obviously, Portable is the most desirable choice for long tail ETL solutions, but there are other enterprise solutions on the market as well.
Portable is a top choice for many companies. Why? It is because it offers perfect data integration for long-tail data sources. There are more than 300 data connectors present at Portable. You'll find the long-tail ETL connectors that Fivetran doesn't have available.
Talend is one of the most accessible places to carry out the ETL process. Why are we saying so? It is because Talend is a place with no-code options. You can carry data transformation and ETL. Do you know what is even better? It is a platform where you can use a completely free plan. But, there are some limitations available with the free plan. Talend offers you comprehensive data management. Also, it has integration with many cloud platforms.
Hevo Data is a data management and integration tool designed to help businesses integrate data from various sources. Hevo Data is a cloud-based platform, customers do not need to worry about installing, configuring, or maintaining the underlying infrastructure.
Hevo allows you to copy data in near real-time from over 150 sources, including Snowflake, BigQuery, Redshift, Databricks, and Firebolt.
Stitch, a data pipeline tool, is included with Talend. It controls data extraction and simple manipulations using a built-in GUI, Python, Java, or SQL. Extra services include Talend Data Quality and Talend Profiling.
Top features are:
Fivetran is a cloud-based data integration platform that assists enterprises in automating data transfer from several sources to a central data warehouse or another place.
Fivetran uses a fully managed, zero-maintenance architecture, which means that tasks such as data translation, data quality checks, and data deduplication are performed automatically.
Top features include: