5 data engineering concepts you need to know
Pipelines, data warehouses, ETL/ELT, orchestration, and data quality are the 5 core concepts of data engineering. How they connect in practice.
In the data-driven world, there are many roles, areas, subsystems, and processes, all of which are vital to advancing a company's data maturity. Data Engineering is a critical part of this process, responsible for preparing all the necessary infrastructure to launch and maintain your data operation, as well as integrating, managing, and preparing massive amounts of information for analysis and actionable insights.
With that in mind, we have put together 5 Data Engineering concepts you need to know to better understand how this area works.
What is Data Engineering
Data Engineering is responsible for making raw data extracted from sources comprehensible and usable. The engineers behind this process collect, identify, store, clean, access, and process data, in addition to building data pipelines and exposing this information so data scientists and analysts can do their jobs.
Erathos has prepared a more in-depth article on this topic, which you can access by clicking here: Engenharia de Dados Para Startups.
If your team lacks the internal capacity to set up these processes from scratch, it is worth considering specialized support. beAnalytic is a data engineering consultancy that can help design the right architecture and accelerate this implementation.
01) Data Warehouse
A data warehouse is responsible for centralizing all of a company's data into a single repository. Here, it is stored, organized, and managed, allowing for subsequent efficient analysis. Think of it as an extensive digital library where you can easily access the information you need, regardless of its source, without having to query multiple sources or waste time sanity-checking information across different databases.
Data stored in a data warehouse is typically structured and optimized to ensure that generating business reports and analyses is a straightforward process. This is because it is designed to handle large volumes of data and facilitate complex queries, as well as integrate data from various sources like flat files, spreadsheets, CRMs, ERPs, and other software that run the day-to-day operations of a company's various departments.
Today, any effective data initiative needs an analytical database—that is, a data warehouse capable of centralizing data so it is accessible for transformations and more complex analysis. The absence of one contributes to the creation of the so-called Data Silos, which happen when information is so scattered and isolated in each department that organizational-level decision-making becomes difficult and time-consuming.
We have released an interesting and highly comprehensive e-book on this topic to help you combat Data Silos within your company. Click here to download it: O que são data silos e porque eles estão acabando com seu crescimento.
02) ELT
ELT stands for Extract, Load, and Transform. In data engineering, it is a set of processes that involves extracting structured or unstructured data from its various sources, loading it into a Data Lakehouse, and then transforming the data into a format that facilitates analysis and consumption.
You should also keep in mind that there is another process called ETL, in which data transformation happens before loading. The primary difference between ELT and ETL is this: while ETL transforms data before loading it into the Data Warehouse, ELT loads the raw data first and then performs the transformation as needed.
When it comes to Data Engineering, understanding what ELT and ETL are is crucial. These techniques allow companies to process large volumes of data quickly and efficiently. By loading raw data first, you can leverage the processing power of your data warehouses to perform transformations at scale.
Furthermore, using ELT allows companies to build a more flexible data model, with a schema that can easily be modified to meet changing analytical and decision-making needs, ensuring plenty of room for innovation and re-analyzing the process when necessary.
03) Data Pipelines
A data pipeline is an automated data engineering process that enables efficient and reliable collection, storage, processing, and analysis of data.
Think of it as a system that transports your company's data from point A to point B, performing several transformation steps along the way (in the case of ETL) or moving raw data directly to the storage system with a defined update frequency (every hour, daily, weekly, etc.).
The importance of a data pipeline for data engineering is that it enables companies to extract valuable insights from their data quickly and efficiently, in addition to enabling real-time filtering of useful information. In other words, data is continuously prepared for analysis, ensuring that companies have access to this information whenever they need it.
Another crucial aspect is that having Data Pipelines is essential for implementing predictive analytics and machine learning, as they can also feed the training of machine learning models that use your data in real time.
04) Data Cleaning
Data Cleaning is an iterative process that involves identifying, defining, and correcting errors, inconsistencies, and inaccurate entries in a dataset. It is a critical step in preparing data for analysis and usage in machine learning models, statistical analyses, and other data engineering applications. The goal of data cleaning is to ensure that data is accurate, reliable, and consistent, so that the conclusions and insights derived from it are trustworthy.
This is a critical process for data engineering, since poorly cleaned datasets can lead to incorrect and inaccurate conclusions, resulting in bad business decisions or malfunctioning machine learning models.
Additionally, large and complex datasets can have errors and inconsistencies that are difficult to detect manually. This is why automated data cleaning tools are increasingly used by data engineers to guarantee data quality.
05) Data Activation
Data activation is a technique used in data engineering to put to use the information stored in a data warehouse or data lakehouse. Basically, it is the process of turning data into actionable insights—that is, information that can be used to improve efficiency, decision-making, and business outcomes.
This is vital for any company trying to be data-driven, as it requires a systematic approach to collecting, storing, and analyzing data to get valuable and actionable business insights. Data activation occurs when data is translated into useful insights so that well-founded decision-making can take place.
With proper data activation, you can make more accurate decisions, optimize processes, and improve the customer experience, boosting company ROI and improving the daily quality of operations.
Data Engineering is fundamental to launching and maintaining a data initiative that is sustainable for your company. In this article, we covered some key concepts and tools that are essential to building an engineering setup that helps accelerate your strategy and make your company increasingly data-driven.
In summary, Data Warehouse, ELT, Data Pipelines, Automation, and Data Activation are essential for data engineering because they allow organizations to process large volumes of data efficiently and extract valuable insights for decision-making.
Keep in mind!
1. The Data Warehouse and Data Lakehouses act as the central source where data is stored and managed.
2. ELT is a modern approach to data transformation that helps simplify data pipeline creation.
3. Data Pipelines are necessary to collect, transform, and integrate data from various sources, allowing users to obtain accurate and timely insights.
4. Data Cleaning is the process of correcting errors, removing duplicates, and ensuring data quality control.
5. Data Activation is a process that enables companies to make better-informed decisions, which drives results and helps increase your company's ROI.
With these technologies, organizations can maximize the value of their data and make strategic decisions based on insights faster than ever before.
Want access to more Data-Driven content? Check out other posts on our blog by clicking here.
In the data-driven world, there are many roles, areas, subsystems, and processes, all of which are vital to advancing a company's data maturity. Data Engineering is a critical part of this process, responsible for preparing all the necessary infrastructure to launch and maintain your data operation, as well as integrating, managing, and preparing massive amounts of information for analysis and actionable insights.
With that in mind, we have put together 5 Data Engineering concepts you need to know to better understand how this area works.
What is Data Engineering
Data Engineering is responsible for making raw data extracted from sources comprehensible and usable. The engineers behind this process collect, identify, store, clean, access, and process data, in addition to building data pipelines and exposing this information so data scientists and analysts can do their jobs.
Erathos has prepared a more in-depth article on this topic, which you can access by clicking here: Engenharia de Dados Para Startups.
If your team lacks the internal capacity to set up these processes from scratch, it is worth considering specialized support. beAnalytic is a data engineering consultancy that can help design the right architecture and accelerate this implementation.
01) Data Warehouse
A data warehouse is responsible for centralizing all of a company's data into a single repository. Here, it is stored, organized, and managed, allowing for subsequent efficient analysis. Think of it as an extensive digital library where you can easily access the information you need, regardless of its source, without having to query multiple sources or waste time sanity-checking information across different databases.
Data stored in a data warehouse is typically structured and optimized to ensure that generating business reports and analyses is a straightforward process. This is because it is designed to handle large volumes of data and facilitate complex queries, as well as integrate data from various sources like flat files, spreadsheets, CRMs, ERPs, and other software that run the day-to-day operations of a company's various departments.
Today, any effective data initiative needs an analytical database—that is, a data warehouse capable of centralizing data so it is accessible for transformations and more complex analysis. The absence of one contributes to the creation of the so-called Data Silos, which happen when information is so scattered and isolated in each department that organizational-level decision-making becomes difficult and time-consuming.
We have released an interesting and highly comprehensive e-book on this topic to help you combat Data Silos within your company. Click here to download it: O que são data silos e porque eles estão acabando com seu crescimento.
02) ELT
ELT stands for Extract, Load, and Transform. In data engineering, it is a set of processes that involves extracting structured or unstructured data from its various sources, loading it into a Data Lakehouse, and then transforming the data into a format that facilitates analysis and consumption.
You should also keep in mind that there is another process called ETL, in which data transformation happens before loading. The primary difference between ELT and ETL is this: while ETL transforms data before loading it into the Data Warehouse, ELT loads the raw data first and then performs the transformation as needed.
When it comes to Data Engineering, understanding what ELT and ETL are is crucial. These techniques allow companies to process large volumes of data quickly and efficiently. By loading raw data first, you can leverage the processing power of your data warehouses to perform transformations at scale.
Furthermore, using ELT allows companies to build a more flexible data model, with a schema that can easily be modified to meet changing analytical and decision-making needs, ensuring plenty of room for innovation and re-analyzing the process when necessary.
03) Data Pipelines
A data pipeline is an automated data engineering process that enables efficient and reliable collection, storage, processing, and analysis of data.
Think of it as a system that transports your company's data from point A to point B, performing several transformation steps along the way (in the case of ETL) or moving raw data directly to the storage system with a defined update frequency (every hour, daily, weekly, etc.).
The importance of a data pipeline for data engineering is that it enables companies to extract valuable insights from their data quickly and efficiently, in addition to enabling real-time filtering of useful information. In other words, data is continuously prepared for analysis, ensuring that companies have access to this information whenever they need it.
Another crucial aspect is that having Data Pipelines is essential for implementing predictive analytics and machine learning, as they can also feed the training of machine learning models that use your data in real time.
04) Data Cleaning
Data Cleaning is an iterative process that involves identifying, defining, and correcting errors, inconsistencies, and inaccurate entries in a dataset. It is a critical step in preparing data for analysis and usage in machine learning models, statistical analyses, and other data engineering applications. The goal of data cleaning is to ensure that data is accurate, reliable, and consistent, so that the conclusions and insights derived from it are trustworthy.
This is a critical process for data engineering, since poorly cleaned datasets can lead to incorrect and inaccurate conclusions, resulting in bad business decisions or malfunctioning machine learning models.
Additionally, large and complex datasets can have errors and inconsistencies that are difficult to detect manually. This is why automated data cleaning tools are increasingly used by data engineers to guarantee data quality.
05) Data Activation
Data activation is a technique used in data engineering to put to use the information stored in a data warehouse or data lakehouse. Basically, it is the process of turning data into actionable insights—that is, information that can be used to improve efficiency, decision-making, and business outcomes.
This is vital for any company trying to be data-driven, as it requires a systematic approach to collecting, storing, and analyzing data to get valuable and actionable business insights. Data activation occurs when data is translated into useful insights so that well-founded decision-making can take place.
With proper data activation, you can make more accurate decisions, optimize processes, and improve the customer experience, boosting company ROI and improving the daily quality of operations.
Data Engineering is fundamental to launching and maintaining a data initiative that is sustainable for your company. In this article, we covered some key concepts and tools that are essential to building an engineering setup that helps accelerate your strategy and make your company increasingly data-driven.
In summary, Data Warehouse, ELT, Data Pipelines, Automation, and Data Activation are essential for data engineering because they allow organizations to process large volumes of data efficiently and extract valuable insights for decision-making.
Keep in mind!
1. The Data Warehouse and Data Lakehouses act as the central source where data is stored and managed.
2. ELT is a modern approach to data transformation that helps simplify data pipeline creation.
3. Data Pipelines are necessary to collect, transform, and integrate data from various sources, allowing users to obtain accurate and timely insights.
4. Data Cleaning is the process of correcting errors, removing duplicates, and ensuring data quality control.
5. Data Activation is a process that enables companies to make better-informed decisions, which drives results and helps increase your company's ROI.
With these technologies, organizations can maximize the value of their data and make strategic decisions based on insights faster than ever before.
Want access to more Data-Driven content? Check out other posts on our blog by clicking here.