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ETL Development in Business Intelligence: Overview

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ETL Development in Business Intelligence - Thinklayer

Using ETL Development and data integration, you may increase performance and make your firm more efficient and lucrative. But what exactly is it? When do you need to put it into action? Continue reading to learn more about ETL development and the answers to these and other issues.

ETL Development

ETL stands for Extract, Transform, and Load. It is a means of transferring data from several data storages into a data warehouse. In general, it refers to the process of moving data from a source database to a destination database.

ETL developers begin this procedure by EXTRACTING raw data from several RDBMS source systems. The data is then TRANSFORMED before being LOADED into a specific data warehouse system. How exactly does it happen? Take a look at this process.

E-EXTRACT

The data is being retrieved from the source system and sent to the staging area. These source systems might be text files, SQL servers, ERP, spreadsheets, or data stores. The staging area provides for the validation of extracted data. It is then sent to the warehouse.

T-TRANSFORM

It is mandatory to clean, map, and convert the extracted data. All of this occurs in the second stage. Several validations occur during this step, including filtering, cleaning, data flow validation, transposing rows and columns, and so on.

L-LOAD

The data is then put into the target data storage systems in the final stage. This is difficult because a vast amount of data must be loaded in a relatively short period of time. The system implements recovery measures and load verifications before the entire process begins.

Uses of ETL Development

One of the most common domains of ETL use is data warehousing. However, there are also more applications for ETL. Take a closer look at this:

Internet of Things data integration

If your company supports IoT then you can utilize ETL to collect data from numerous IoT sources and consolidate it in one place. It will consequently make analyzing the data much easier for you.

Marketing data integration

ETL may help you collect all of your marketing data in one location. This procedure may include marketing and consumer data, online analytics, social networking data, online data marts, and so forth.

Artificial intelligence and machine learning

Also, with the assistance of your data science team, you may consolidate data and prepare it for the construction of a machine learning model. 

Cloud migration

Cloud migration is the process of migrating your data from your premises to the cloud. ETL is a technique for dealing with this procedure. 

Database replication

You can use ETL to transfer data from your source databases to the cloud warehouse. Aside from that, you may require ETL if the amount of your data is continually increasing. 

ETL Tools

An ETL tool employs a procedure that accepts data from various Relational Database Management Systems. It also universally transforms it to make it understandable and loads it into the data warehouse in a neat clean structure ready for analysis.

ETL Development tools offer a variety of capabilities to help with workflow. Therefore with the growing popularity of ETL tools, the data warehousing industry has recognized the significance of various emerging and commercial appliances.

There are multiple tools available these are:

Cloud-Based Tools are

ETL Tool Function

ETL tool-based data warehouses use staging, data integration, and access layers to execute their purpose. It’s a three-tiered design.

ETL Tools for Data Science

Finding the correct ETL tool may take some effort initially, but it will pay off in the long term. Spending a little additional effort early is far preferable to having to rethink your entire data transformation process later on, especially if you’re already running at scale.

If you have clear, well-defined objectives then it won’t be difficult to choose the ETL technology that best matches your demands. Given below are some of the greatest ETL Development tools for data science that you may put to use in your company.

1. Mozart Data

While most data firms focus on a specific aspect of the data analysis process, Mozart enables you to effortlessly integrate all of those tools into a single contemporary data stack. This makes combining and cleaning data easier than ever. Mozart lets the “business people” get direct access to the information they need and when they need it.  

Pros

Cons

2. Xplenty

Xplenty is a cloud-based ETL system that offers simple data streams for automating data flows from diverse sources. It is also in charge of data flows and data scheduling. Customers may use best practices to clean, standardize, and transform their data. It also allows for data consolidation and preparation for Business Intelligence (BI).

Pros

Cons

3. Talend

Talend is an open-source ETL data integration tool. The Talend system may access data from both on-premises and cloud sources. It also has many built-in components that allow for the connecting of various data sources, increasing productivity and reducing the time necessary for data distribution.

Pros

Cons

​4. Improvado

​Improvado is one of the greatest ETL solutions since it is versatile and simple to use. It makes it simple for marketers to collect all of their data in one place. As a result, no coding is required for data automation. Because of its simplicity, there is no requirement for implementers or data experts to assist you in making adjustments.

Pros

Cons

5. Alooma

Alooma is a data migration ETL solution for cloud data warehouses along with unique built-in data integration features. It also has a live data streaming display, a coding sidebar, and other user-friendly features. The most significant advantage of Alooma is that it automates the majority of data flow, enabling you to focus on the results rather than the technical details.

Pros

Cons

6. Starfish

Because of its capacity to quickly give actionable consumer insights, Starfish is an excellent ETL tool for marketing. It also helps firms improve customer targeting by pooling CRM data and obtaining insights about their customers. The Starfish dashboard is simple to use and hence supports customers in developing relevant marketing reports.

Pros

Cons

7. Jaspersoft

Jaspersoft is now one of the most effective business intelligence software solutions on the market. It intends to serve one or more developers while scaling to the most significant levels of data quantity and process complexity. Furthermore, this ETL tool can work in tandem with Hadoop, Google Analytics, and Cassandra.

Pros

Cons

8. Supermetrics

Supermetrics enables users to collect data from various social marketing channels, such as Google Ads, Facebook, and other social media platforms, and direct it to a single destination, such as Google Data Studio, where analytics modules can provide insights into customer preferences for specific goods and services.

Pros

Cons

9. Domo

Domo is one of the most complete analytics tools available currently on the market. It takes data from multiple sites and channels. Also, it provides these data to clients in a simple fashion. The application also has a graphical interface that allows users to merge any number of data sources. Consequently, the customers may create new integrated datasets through a series of drag-and-drop processes.

Pros

​Cons

10. Datorama

Datorama is an ETL marketing solution that takes a comprehensive approach to gather a company’s advertising data. Thus obtaining information from a range of sources allows you to run more effective sales and marketing operations. The software’s three main engines are data integration, activation, and insights.

Pros

Cons

ETL Testing

Testing refers to the process of confirming, verifying, and certifying data while avoiding duplicate records and data loss. ETL testing guarantees that data is transferred from disparate sources to the central data warehouse following transformation rules. It also ensures that all validity checks are followed.

Eight stages of the ETL testing process

Effective ETL testing identifies issues with the source data before placing the data into the data repository. As well as it identifies inconsistencies or ambiguities in the business rules that are supposed to govern data transformation and integration. 

1. Identify business requirements —

Create the data model, describe the business flow, and determine data reporting requirements based on client expectations. It’s critical to start here so that the project’s scope is well defined, documented, and thoroughly understood by testers.

2. Validate data sources —

Perform a data count check to ensure that the table and column data types fulfill the data model’s criteria. Check that check keys are in place. Also, remove the duplicate data.

3. Design test cases —

Create ETL mapping scenarios, SQL scripts, and transformational rules. It is also necessary to check the mapping document to ensure that it has all of the necessary information.

4. Extract data from source systems —

Identify types of bugs or defects encountered during testing and make a report. It is important to detect and reproduce any defects, report, fix the bug, resolve, and close the bug report.

5. Apply transformation logic —

Ascertain that data is changed to conform to the schema of the destination data warehouse. Also checks the data threshold, alignment, and data flow. It ensures that the data type for each table matches the mapping document.

6. Load data into target warehouse —

Before and after moving data from staging to the data warehouse, perform a record count check. Ensure to reject incorrect data and to accept the default values.

7. Summary report —

Check the summary report’s layout, choices, filters, and export capability. This report informs stakeholders of the specifics and results of the testing process. It also gives reason for the incomplete steps, i.e. “out of scope.”

8. Test Closure —

File test closure.

ETL Development Process

We use a systematized ETL development cycle at Thinklayer to provide consistent, on-time, and cost-effective outputs.

  1. Analysis: At Thinklayer, your ETL project begins with planning, research, and reference examination. We check over the materials that our clients have brought with them.
  2. Specification: Thinklayer places a strong emphasis on the specs stage, allowing us to deliver the product on schedule and within budget.
  3. Design: We create quick and efficient models and present you with various mock versions. We do it until you get the one that best meets your demands.
  4. Development: The development step of your ETL project is simplified thanks to our team of skilled developers.
  5. Testing: After each milestone, we put the project through a set of tests.
  6. Delivery: It is the last step, in which the client receives the finished work. The step is repeated until the provided ETL package functions flawlessly in the live environment.

ETL Design

It is difficult to design an ETL cycle. Following these best practices would assure the ETL solution’s effective design and execution.

Analyzing Source Data

Fixing Data Issues

Validation of Logics

Optimizing the ETL Solution

Error Handling, Logging, and Alerting

Point of Failure Recovery

Scheduling, Auditing & Monitoring ETL Jobs

ETL in Data Warehouse

ETL is a Data Warehousing procedure. An ETL tool collects data from numerous data source systems, transforms it in the staging area, and then loads it into the Data Warehouse system. Let’s go through each stage of the ETL process in detail

1. Extraction: 


In this stage, data from numerous source systems is extracted into the staging area in various formats such as relational databases, No SQL, XML, and flat files. It is critical to first extract data from multiple source systems and stores it in the staging area before transferring it to the data warehouse.

This is due to the fact that the retrieved data is in multiple formats and might potentially be corrupted. As a result, importing information straight into the data warehouse may cause it to be damaged, and rollback will be much more difficult. As a result, this is one of the most crucial processes in the ETL process.

2. Transformation: 


In this stage, the extracted data is subjected to a set of rules or functions in order to be converted into a single standard format. It might entail the following processes/tasks:

3. Loading: 


In this stage, the transformed data is eventually loaded into the data warehouse. The data is sometimes updated very often by entering it into the data warehouse, and other times at longer but regular intervals.

The ETL development process may also take advantage of the pipelining notion, which means that as soon as some data is extracted, it can be converted, and fresh data can be extracted at that time. Furthermore, as the modified data is being imported into the data warehouse, the extracted data might be altered.

Conclusion 

ETL Development helps in taking decisions by analyzing data. It can handle complex problems which cannot be handled by traditional databases. It also provides a common data repository and loads data from different sources into the target database.

ETL tools are necessary for businesses to efficiently manage their data and present it in a way that’s easier to analyze. These tools will also allow you to discover valuable insights into the needs of your customers. 

Using quick data transformation and migration capabilities, ETL tools may help you aggregate data from a variety of platforms, such as social networking websites, datasheets, and end-user PCs, into a single data warehouse.

So, start using ETL development and data integration services for the growth of your business. At Thinklayer we are providing you with the best services to integrate ETL services to your data warehouse for better outcomes. 

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