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How do you extract, transform, and load data effectively?

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Data warehousing is a process of collecting, organizing, and analyzing data from various sources for business intelligence and decision making. One of the key steps in data warehousing is extract, transform, and load (ETL), which involves moving data from different systems to a central repository, applying transformations to make it consistent and usable, and loading it into the data warehouse. In this article, you will learn how to perform ETL effectively and efficiently, and what are some of the best practices and tools to use.

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Georg Sinn

Driving Data Excellence | Data Management Specialist | ????#people + ???#data = ??#future
Jaswanth Kotagiri

SAP BTP Integration |CPI | BOOMI INTEGRATION

1 Extract data

The first step in ETL is to extract data from the source systems, such as databases, files, APIs, or web services. Depending on the type and frequency of the data, you may need to use different methods and techniques to extract it, such as full or incremental extraction, change data capture, or web scraping. The main goals of extraction are to minimize the impact on the source systems, ensure data quality and integrity, and handle errors and exceptions.

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Georg Sinn

Driving Data Excellence | Data Management Specialist | ????#people + ???#data = ??#future
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Especially dealing with file-based sources, you need to understand how those files are generated and provided. Who is responsible for creating and sharing the file? Clear ownership on the source side, delivery schedules and methods if the file is sent to you, or document where the file is located and how to access it. Plan resources and handling accordingly. Also have a contingency if files can't be processed or accessed for any reason. Naming patterns and location standards are also important to establish upfront. Define consistent file naming and organization to avoid confusion down the line. Standardize on formats and encoding as well - you'll need this metadata for proper extraction.

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2 Transform data

The second step in ETL is to transform the extracted data to make it suitable for the data warehouse. This may involve applying various operations, such as cleansing, filtering, joining, splitting, aggregating, standardizing, or enriching the data. The main goals of transformation are to ensure data consistency and compatibility, improve data quality and accuracy, and enhance data value and usability.

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3 Load data

The third step in ETL is to load the transformed data into the data warehouse, which is typically a relational database or a cloud-based platform. Depending on the design and architecture of the data warehouse, you may need to use different methods and techniques to load the data, such as bulk or batch loading, trickle or streaming loading, or staging or direct loading. The main goals of loading are to optimize the performance and scalability of the data warehouse, ensure data security and compliance, and enable data access and analysis.

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4 Choose ETL tools

To perform ETL effectively and efficiently, you may need to use various tools and software that can automate and simplify the process, such as ETL frameworks, pipelines, or platforms. Some of the factors to consider when choosing ETL tools are the complexity and volume of the data, the frequency and latency of the ETL process, the compatibility and integration of the source and target systems, the functionality and flexibility of the tools, and the cost and maintenance of the tools.

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5 Follow ETL best practices

To ensure the quality and reliability of the ETL process, you may need to follow some of the best practices and guidelines, such as documenting the ETL requirements and specifications, designing the ETL logic and workflow, testing and validating the ETL results, monitoring and logging the ETL performance, and troubleshooting and resolving the ETL issues.

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Jaswanth Kotagiri

SAP BTP Integration |CPI | BOOMI INTEGRATION
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firstly when need to find out what are source and target system for ETL because each systems have different capabilities of loading data for eg:SAP ERP when you load data from SAP ERP we have access only Application layer where you can`t implement any functions at this level as its will have impact of data loading if we do so. 2)Have discussion with stakeholders and load tables which are required. 3) when you load high volume of data tables make sure you do datatype conversion and custom functions at minimal level. Load high volume of data after business hours So that it won`t impact job servers. 4) Monitor the jobs which are taking more time than usual and enable email notifications on job failures.

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6 Learn ETL skills

To master the ETL process, you may need to learn some of the essential skills and knowledge, such as data modeling and schema design, SQL and scripting languages, data quality and validation techniques, ETL tools and platforms, data warehouse concepts and architectures, and business intelligence and analytics tools. You can learn these skills through online courses, books, blogs, podcasts, or communities.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you extract, transform, and load data effectively?

1

2

3

4

5

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1 Extract data

2 Transform data

3 Load data

4 Choose ETL tools

5 Follow ETL best practices

6 Learn ETL skills

7 Here’s what else to consider

Data Warehousing

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How do you extract, transform, and load data effectively?

1

2

3

4

5

6

7

1 Extract data

2 Transform data

3 Load data

4 Choose ETL tools

5 Follow ETL best practices

6 Learn ETL skills

7 Here’s what else to consider

Data Warehousing

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