ETL stands for extract, transform, and load, a process of moving data from various sources to a centralized data warehouse or database. ETL skills are essential for database engineers who want to design, build, and maintain efficient and reliable data pipelines. In this article, you will learn some effective strategies for developing ETL skills, such as:
Before you dive into the details of ETL, you need to understand the basics of data, databases, and data warehousing. You should be familiar with the concepts of data types, data models, data quality, data normalization, and data integration. You should also know how to use SQL, the standard language for querying and manipulating data in relational databases. SQL will help you perform various ETL tasks, such as extracting data from source tables, transforming data with functions and joins, and loading data into target tables.
ETL (Extract, Transform, Load) talks about making data usable for organizations. The 3 key areas for the pipeline are Sources, Transformations/Manipulations, and Destinations. One should have a decent understanding of Databases (SQL programming) which are one of the primary sources of Data and Python for data manipulation/cleaning to begin with. With these 2 skills, one can kick-start ETL/ELT pipelines. Understanding the distinction between OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) systems is crucial. Whatever may be technologies adopted by the organizations only the software/tools may vary, but the core concept of data movement/availability from source to destination remains the same.
??Master the Fundamentals: ~ Begin by establishing a strong foundation in data fundamentals, including types, models, and warehousing concepts. ??SQL Proficiency: ~SQL became my trusty sidekick in the ETL realm. Mastering it not only unlocked the power of data manipulation but also paved the way for seamless extraction, transformation, and loading tasks. ??Data Quality Matters: ~ I've learned that meticulous attention to data quality ensures the integrity and reliability of the entire ETL pipeline.
When I first learned the basics, I search for the most used tools and concepts on Medium. Through Medium articles, I was able to learn SQL, more about dbt, and best practices for working with the modern data stack.
There are various ways to develop ETL skills, - understand the fundamentals of data - learn SQL and keep on learning - understand data warehousing concepts - learn any ETL tool - learn the basics of programming - learn error handling and logging - learn cloud platform - learn automation and scheduling - build and implement real project - stay updated
-Strong SQL Fundamentals especially on inner, outer join and complex queries and practice it. -Understand Data models, Fact, Attributes, Data Dimensions to the core. - End of the day, all ETL tools foundations are the same. Strong Fundamentals leads to Strong Foundation.
There are many ETL tools available in the market, each with its own features, benefits, and drawbacks. You should choose an ETL tool that suits your needs, preferences, and budget. Some of the popular ETL tools are SSIS, Informatica, Talend, Pentaho, and Airflow. You should learn how to use the ETL tool of your choice, including its interface, components, functions, and best practices. You should also be able to compare and contrast different ETL tools and evaluate their performance and scalability.
ETL/ELT are now the core of any data driven business. There are plenty of tools available in market which helps in settings up the data flow. Each tool has its own pros and cons to be selected for specific purpose. But the fundamentals remain always same. For enabling any ETL flow below approach with always remain segnificant. 1. Domain /Business understanding 2. Brainstorming with business to under the insights and purpose of collecting data. 3. Data modelling - start with conceptual modelling then move to logical and physical modelling. 4. Selection of ETL tools and DB 5. Implementation of Data warehouse philosophies using selected tools. 6. Implementation of Data quality and data governance strategies. 7. Crating Data warehouse.
This is an important one , what tool you choose among the plethora of tools.Few things you would want to consider: 0. Business rules i.e. requirements , which tool fits your use case, shortlist them 1. Prefer ELT over ETL and leverage the compute and storage of cloud or cloud data warehouses. No overhead to install , maintain, patch blah blah. A SaaS App essentially. 2. Check connectivity : On Prem connectors along with cloud connectivity Major API connectors like Google analytics, SharePoint,file systems along with an option to create and connect to any web based application via API 3. Security and masking features,user confirmation,access control 4. Cost- enterprise solutions or open source or low code which one fits the wallet
Try some data manipulation libraries in your language of choice before you jump on to an ETL tool, this will give you a greater appreciation for any ETL tool you eventually settle on. As far as deciding on what tool to use, pick something that you think will be quicker to learn, how to decide that is entirely up to you, it could be that the tools uses your programming language like Airflow for python users or SSIS if your already well versed in SQL. Id rather leverage a tool i already have some familiarity with to save time, you can always go back and learn new technology.
I have tried to find ETL tools to do the work for me. It always seems like I need to build my own toolchain because ETL is such a custom type of work.
For MSSQL shops use SSIS - integrated services. The following guidelines will help you develop the highest performing SSIS packages : 1) BULK INSERT and VIEWS are your friend. Row by row INSERTs are your enemy. Once the data is all loaded, create clustered and nonclx indexes on staging tables 3) before committing to all of the neat workflow Tasks available in ETL design, make sure a Task (like fuzzy logic lookup) don’t prevent you from issuing BULK INSERTS and forcing an INSERT for each row of data. 4) Always use a VIEW as a source and destination for SQL data. OLD SCHOOL BCP (bulk procedure copy) practices still is the best way to load raw data. SSIS is just prettier. Want to jump in: optimize and fix existing code in a test env.
The best way to develop ETL skills is to practice with real-world scenarios. You can find many online resources that offer ETL projects, challenges, and exercises, such as Kaggle, DataCamp, and SQLZoo. You can also use your own data or public data sets to create your own ETL projects. You should try to solve different types of ETL problems, such as data cleansing, data transformation, data validation, data loading, and error handling. You should also document your ETL process and explain your logic and reasoning.
?? Learning: - Formal Education: CS degrees provide foundational skills. Explore Master's courses in Data Science and Analytics for in-depth ETL expertise. - Online Resources: Dive into advanced tutorials on YT. Explore GH repositories for practical ETL projects and contributions. MIT OpenCourseWare offers a wealth of knowledge. - AI-Assisted Learning: Use tools like ChatGPT for tailored skill development and project guidance. ?? Practical Experience: - Entrepreneurs: Build a product/service focused on ETL. Start with an MVP and evolve. - Internal Opportunities: Express interest in ETL projects within your organization to gain hands-on experience. - Certifications: Acquire certs, especially in specialized areas like PyTorch.
Learn with dirty data. Curated datasets are great for exploratory data analysis (EDA), but your trying to learn what, when and how to use ETL tools, dirty data is great for that. Also don't be afraid to use unstructured data as well, most of the data in the real world is unstructured, getting to grasps with this reality early will set you up better for later on in your career.
Following Microsoft best practices is critical to achieving high performance SSIS ETL. Microsoft ETL solution, provides BI / db developers with many different cool and interesting workflow and data transformation tasks. At a price…. Microsoft has a long history of “ease of use comes at the cost of performance” - ETL technology is no different. Avoid row by row INSERTs - for optimal load performance we want to make sure BULK INSERT is being used. SHORT VERSION FROM AN SSIS VETERAN Blast everything into a staging heaps, then create a clustered and non-clustered indexes on those heap and start issuing UPDATES and DELETES for transformations. Lastly, use a separate database for ETL staging with recovery mode BULK LOGGED.
Engage in hands-on practice by working on real-world projects or using sample datasets. This helps reinforce theoretical knowledge and builds practical skills.
From my experience, I enrolled in online courses and webinars offered by experts in the field. Platforms like Coursera, Udacity, and LinkedIn Learning often feature courses created by industry professionals. These courses can provide structured learning and real-world examples. To include the Microsoft learning portal and AWS portal with real-time labs.
Another effective strategy for developing ETL skills is to learn from others. You can join online communities, forums, and blogs that focus on ETL topics, such as Stack Overflow, Reddit, and Medium. You can ask questions, share your insights, and get feedback from other ETL enthusiasts and experts. You can also read books, articles, and tutorials that cover ETL concepts, techniques, and best practices. You can also watch videos, podcasts, and webinars that demonstrate ETL examples and solutions.
Ask questions, always ask questions, no one person knows everything. I would be worry of having someone in my team who never asks for help, we all make mistakes we can learn from them together.
Collaborate with data engineering teams to understand best practices and learn from experienced professionals in the field. You can join online communities, forums, and blogs that focus on ETL topics, such as Stack Overflow, Reddit, and Medium.
I would recommend to participate in online forums and communities dedicated to data engineering, ETL, and related topics. Platforms like Stack Overflow, Reddit (e.g., r/DataEngineering), and LinkedIn groups that often have discussions and Q&A sessions where professionals share their experiences and insights.
Another way is having a Mentor Friend. Someone with a Big technical experience and loving to share his story, tips, success and failures. He can guide you if you are in a situation where you dont know what to do or how to react !
ETL is a dynamic and evolving field that requires constant learning and updating. You should keep up with the latest trends and developments in ETL, such as cloud-based ETL, big data ETL, and streaming ETL. You should also be aware of the emerging tools, technologies, and frameworks that support ETL, such as Spark, Kafka, and Hadoop. You should also explore new opportunities and challenges that ETL presents in different domains and industries, such as e-commerce, health care, and finance.
1- To implement ETL you should learn SQL or either Python(PySpark). 2- Observe the modern architecture i:e Medallion Architecture for ETL in which we create bronze(raw), silver(Cleaned), gold(Aggregated) layers on internet Note: Observe the Data Analytics Architecture of Azure, AWS to absorb the concept quickly. 3- Try to implement those concepts with any of the language SQL or PySpark. PS: Do ping me if need any references material regarding these concepts
I recognize the importance of staying updated in the dynamic field of ETL. Cloud-based ETL, big data ETL, and streaming ETL are key trends to follow. Familiarity with tools like Spark, Kafka, and Hadoop is crucial for professionals in this evolving landscape. Exploring ETL applications in diverse industries like e-commerce, health care, and finance opens up new opportunities. Continuous learning is the key to staying relevant in the ever-changing world of data analysis. #ETLTrends #DataAnalysis #ContinuousLearning
I always want to understand the business value of any ETL initiative before I start work. There's often secondary business values that can be added onto the main value when doing ETL.
Extract, transform, and load projects, make disparate data come together, and therefore provide better intelligence for the company ETL is a necessary evil, and not something that is a calculated “value-add business decision”. Either the user can be provided with the information that helps them best perform their jobs, or the information is not provided. It’s an on or off all or none situation. With the low cost of high end storage, be like the NSA - store everything. Ex: cache data returned JSON https calls - the cost of retrieving data repeatedly WILL be higher than storing it. Wisdom tells us that when your customer uses the word “maybe” to describe a relevant data point, the answer is “yes , design for it just in case.“
ETL technologies evolve, so stay updated on the latest trends and advancements. You should also be aware of the emerging tools, technologies, and frameworks that support ETL, such as Spark, Kafka, and Hadoop. Follow industry blogs, attend webinars, and participate in relevant communities.
Estudia y aplica adicional técnicas de validación automatizada de datos para cada etapa del proceso ETL, y también usa herramientas de este tipo como IBM infoesfera o ssis de microsoft
In ETL, two principles stand out from my experience. First, adhering to best practices and solid design principles is vital for creating robust, maintainable, and scalable ETL processes. This approach leads to efficient systems that are less error-prone and easier to manage. Second, the ability to flexibly and seamlessly integrate new data sources is key. As data integration is ever-evolving, ETL systems must adapt without disrupting existing functions, ensuring scalability and reducing the need for major overhauls.
In my experience, to really get a grip on ETL, you should dive into some real-data projects. Nothing beats the learning you get from making pipelines or the actual problems with raw and real-world data. And don't forget to keep an eye on the new stuff coming out in tech. Maybe there is some new tool that can help write the same pipeline easily.
There are many to consider how ETL design is going to help your company to serve business activities in future 1. Understand current data volume, growth of data volume, server/ users capacity, learn its growth in recent years, identify how similar business and their users /customer volume and their tools/vision? 2. Understand your company business growth /new products & business applications expected in future years, its impact in data volumes, estimation of technology support/usages/changes in coming years 3. Do platform currency/current ETL tools capability, exponential data growth can impact your ETL design , future Data vision? 5. Have you discussed above all points with stakeholders/Architects/business application owner/business SMEs?
Dive into ETL mastery like a chef crafting a signature dish: blend foundational SQL expertise with the sauté of data extraction, whisk in the transformative magic of scripting languages, and simmer in the art of schema design. Infuse your process with a pinch of error-handling spice, ensuring a delectable dish of reliable data. Finally, garnish with a commitment to continuous learning, creating a unique flavor that sets your ETL skills apart.