In almost every company, the potentially utilizable data is inaccessible; it was revealed in a study that 2/3rd of businesses either have little or no benefit from their data. The data remains locked in legacy systems, isolated silos, or scarcely used applications.
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ETL is the abbreviation of Extract, Transform and Load. It extracts the data from different sources and converts it into an understandable format. This data is used for storing in a database and used for future reference.
The data from multiple sources is extracted and this data is further copied to the existing data warehouse. When handling huge volumes of data and many source systems, the data is combined into a single data store.
ETL is used to transfer data from an existing database to another database, This is the only process involved in loading the data to and from data warehouses and data marts.
Representation of ETL Workflow
One of the big trends over the last few years is to have ETL delivered in the cloud. The question is, how does ETL work on cloud-based architecture when the data is often on-premise?
If the data is on-premise then the data processing is on-premise, likewise, if the data is off-site then the data processing should be in an off-site data center.
Traditional ETL tools followed a three-tier architecture, which means they are split up into three parts, they are:
ETL Three Tier Architecture
All these three layers are designed to work within the four walls of your organization. To cloud-enable, these platforms in an on-premise scenario, the two functions of the user interface and metadata repository are taken to the cloud.
However the processing engine stayed on-premise, so when the processing engine was supposed to operate, it would receive the appropriate commands and information from the cloud metadata repository.
The processing engine would run that data movement routine on-premise, this allows the data to live where it natively is rather than requiring all the data to move to the cloud.
When something needs to be run in the cloud then another engine in the cloud would run that data. The storage and design of the ETL movement are hosted by the cloud ETL vendor but the engine that processes the commands can sit in multiple locations.
The process of merging data from various sources into a single view is known as data integration. Starting from mapping, ingestion, cleansing, transforming to a destination sink, and making data valuable and actionable for the individual who accesses it.
Talend offers strong data integration tools for performing ETL processes. As data integration is a complex and slow process, talent solves the problem by completing the integration jobs 10x faster than manual programming with a very low cost.
Talend data integration has two versions they are:
The most powerful open-source data integration tool available in the market is Talend open studio. This ETL tool helps you to effortlessly manage various steps involved in an ETL process, starting from the basic design of the ETL till the execution of ETL data load.
Talend open studio is based on a graphical user interface using which you can simply map data between the source and target areas. All you need to do is select the required components from the palette and place them into the workspace. It also offers you a metadata repository from where you can simply reuse and repurpose the work; this process will help you increase productivity and efficiency over time.
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Ease of Use
ETL tool is very easy to use as the tool itself identifies data sources and the rules for extracting and data processing. This process eliminates the need for manual programming methods, where you are required to write the code and procedures.
Visual Data Flow
To represent the visual flow of the logic, GUI is required. The ETL tools are based on Graphical User Interface which enables you to specify instructions using a drag-drop method to represent the data flow in a process.
Most of the data warehouses are delicate and many operational problems arise. To reduce these problems ETL tools possess in-built debugging functionality which enables data engineers to build on the features of an ETL tool to develop a well-structured ETL system.
Simplify Complex Data Management Situations
Moving large volumes of data and transferring them in batches becomes easier with the help of ETL tools. These tools handle complex rules and transformations and assist you with string manipulations, calculations, and data changes.
Richer data cleansing
ETL tools are equipped with advanced cleaning functions when compared with ones present in SQL. These functions serve to the requirements of complex transformations which usually occur in a complex data warehouse.
The overall structure of an ETL system minimizes the efforts in building an advanced data warehousing system. Additionally, many ETL tools emerge with performance-improving technologies like Massively Parallel Processing, Cluster Awareness, and Symmetric Multi-Processing.
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ETL tools allow organizations to make their data meaningful, accessible, and usable across diverse data systems. Choosing the right ETL tool is crucial and complex as there are many tools available.
As there are many ETL tools available, we have divided them into four categories according to the organization needs:
Open-Source ETL tools
Similar to other aspects of software infrastructure, ETL has a huge demand for open source tools and projects. These open-source tools are created for maintaining scheduled workflows and batch processes.
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Cloud-native ETL tools
With most of the data moving to the cloud, Many cloud-related ETL services started to evolve. Few of them stick to the basic batch model while others start to offer intelligent schema detection, real-time support, and more.
Real-time ETL tools
Performing your ETL in the mode of batches makes sense only when you are not in need of real-time data. This batch process works better for tax calculations and salary reporting. Although, all modern applications need real-time data access from various sources. For instance, when you upload an image to your Instagram account, you want your friends to notice it immediately, not a day later.
Batch ETL tools
Almost every ETL tool in the world is based on batch processing and on-premise. In the past, most organizations used to utilize their database resources and free computing to perform overnight batch processing of ETL jobs and consolidate data during off-hours.
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Every day the organizations get huge volumes of data through inquiries, emails, and service requests. For an organization, it becomes a priority task to handle the data efficiently to ensure success.
The future of the organization depends on how well they handle the data to maintain healthy customer relationships. Managing data becomes easier with the help of ETL tools which improve data processing and increase productivity.
The most desired job profiles related to Talend are Talend ETL developer, Talend developer, and Talend Admin. There are many job profiles available in the domain of talent as it is a rewarding career path and has the best opportunities in Big Data.
There is a great demand for job aspirants with ETL skills due to the need for large data handling efficiency. According to the Ziprecruiter website, the average salary quoted for a Talend ETL developer in the USA is $126,544 per year.
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Ravindra Savaram is a Technical Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.
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