In the data-driven world, there are many roles, areas, subsystems, and processes, all very important for advancing a company's data maturity. Data Engineering is a vital part of this process, as it is responsible for preparing all the necessary infrastructure to launch and maintain your data operation, as well as integrating, managing, and preparing large amounts of information for analysis and the quest for actionable insights.
With that in mind, we have separated 5 concepts of Data Engineering that 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 its sources, understandable and usable. The engineers responsible for this process are tasked with collecting, identifying, storing, processing, and accessing the data, as well as creating data pipelines and presenting this information for scientists and analysts to act upon.
Erathos has prepared a more in-depth article on this topic, which you can access by clicking here: Data Engineering for Startups.
01) Data Warehouse
A data warehouse is responsible for centralizing all a company's data in a single repository. Here, they will be stored, organized, and managed, allowing for efficient analysis to be performed later. It is like an extensive digital library, where you can access the information you need, regardless of its source, and easily, without having to consult various sources or waste time corroborating information with multiple different databases.
The data stored in a data warehouse is usually structured and optimized to ensure that business reporting and analysis is a simple process. This occurs because it is designed to handle large volumes of data and facilitate complex analyses, in addition to integrating data from various sources such as files, spreadsheets, CRMs, ERPs, and other software that ease the daily operations of various areas of a company.
Currently, every effective data initiative needs to have an analytical database capable of centralizing data, so that it is accessible for transformations and more complex analyses. The absence of one contributes to the formation of so-called Data Silos, which occur when information is so sparse and focused on each area that organizational-level decision-making is difficult and time-consuming.
We launched an interesting and comprehensive e-book on this subject to help you combat Data Silos within your company. Click here to download: What are data silos and why they are hindering your growth.
02) ELT
ELT is an acronym that stands for, in English, Extract, Load, and Transform. Within data engineering, it is a set of processes that involves the extraction of structured or unstructured data from various sources, followed by loading it into a Data Lakehouse, and then transforming the data into a format that facilitates analysis and its use.
You should also keep in mind that there is another process called ETL, in which the transformation of data occurs before loading. The main difference between ELT and ETL is this: while ETL transforms data before loading it into the Data Warehouse, ELT first loads the raw data and then performs the transformation as needed.
When it comes to Data Engineering, understanding what ELT and ETL are is important. These techniques allow companies to process large volumes of data quickly and efficiently. By loading raw data first, it is possible to utilize the processing capabilities of data warehouses to perform large-scale transformation.
In addition, using ELT allows companies to create a more flexible data model, with a structure that can be easily modified to meet changing analysis and decision-making needs, ensuring greater space for innovation and re-analysis of the process when necessary.
03) Data Pipelines
A data pipeline is an automated process in data engineering that enables the collection, storage, processing, and analysis of data efficiently and reliably.
It’s like a system that transports your company's data from point A to point B, performing various transformation stages along the way (in the case of ETL), or sending raw data directly to the storage system, with a defined update frequency (every hour, every day, every week…).
The importance of a data pipeline for data engineering is that it allows companies to extract valuable insights from their data quickly and efficiently, in addition to enabling the filtering of useful information in real-time. In other words: data is continuously prepared for analysis, ensuring that companies have access to this information whenever they need it.
Another very important point is that having Data Pipelines is essential for implementing predictive analysis and machine learning, as they can also assist in training machine learning models that utilize your data in real-time.
04) Data Cleaning
Data Cleaning is the iterative process that involves identifying, defining, and correcting errors, inconsistencies, and inaccurate entries in a dataset. It is a very important step in preparing data for analysis and use 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 conclusions and insights derived from it are precise and trustworthy.
This is an important process for data engineering since poorly cleaned datasets can lead to incorrect and imprecise conclusions, resulting in wrong business decisions or machine learning models that do not function properly.
Furthermore, large and complex datasets can have errors and inconsistencies that are difficult to detect manually, which is why the use of automated data cleaning tools is increasingly utilized by data engineers to ensure data quality.
05) Data Activation
Data activation is a technique used in data engineering, aimed at using the information stored in a data warehouse or data lakehouse. Essentially, it is the process of transforming data into actionable insights, in other words, into information that can be used to improve efficiency, decision-making, and business outcomes.
This is very important for any company looking to be data-driven, as it is necessary to apply a systematic approach to collect, store, and analyze data to gain valuable and actionable insights for the business. Data activation occurs when data is transformed into useful insights so that well-founded decision-making takes place.
With proper data activation, it is possible to make more precise decisions, optimize processes, and enhance customer experience, increasing the company’s ROI and improving the daily quality of what is done.
Data Engineering is fundamental to launching and maintaining a data initiative that is sustainable for your company. In this article, we brought some key concepts and tools that are essential to have an engineering framework that helps accelerate your strategy and make your company increasingly data-driven.
In summary, Data Warehouse, ELT, Data Pipelines, Automations, 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.
Remember!
1. The Data Warehouse and Data Lakehouses function as the central point where data is stored and managed.
2. ELT is a modern approach to data transformation that helps simplify the creation of data pipelines.
3. Data Pipelines are necessary to collect, transform, and integrate data from various sources, enabling users to gain timely and precise insights.
4. Data Cleaning is the process of correcting errors, removing duplicates, and ensuring the quality control of available data.
5. Data Activation is a process that enables companies to make better-informed decisions, driving results and helping to increase your company's ROI.
With these technologies, organizations can maximize the value of their data and make strategic decisions based on insights more quickly.
Want access to more Data-Driven content? Explore the other posts on our blog by clicking here.
In the data-driven world, there are many roles, areas, subsystems, and processes, all very important for advancing a company's data maturity. Data Engineering is a vital part of this process, as it is responsible for preparing all the necessary infrastructure to launch and maintain your data operation, as well as integrating, managing, and preparing large amounts of information for analysis and the quest for actionable insights.
With that in mind, we have separated 5 concepts of Data Engineering that 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 its sources, understandable and usable. The engineers responsible for this process are tasked with collecting, identifying, storing, processing, and accessing the data, as well as creating data pipelines and presenting this information for scientists and analysts to act upon.
Erathos has prepared a more in-depth article on this topic, which you can access by clicking here: Data Engineering for Startups.
01) Data Warehouse
A data warehouse is responsible for centralizing all a company's data in a single repository. Here, they will be stored, organized, and managed, allowing for efficient analysis to be performed later. It is like an extensive digital library, where you can access the information you need, regardless of its source, and easily, without having to consult various sources or waste time corroborating information with multiple different databases.
The data stored in a data warehouse is usually structured and optimized to ensure that business reporting and analysis is a simple process. This occurs because it is designed to handle large volumes of data and facilitate complex analyses, in addition to integrating data from various sources such as files, spreadsheets, CRMs, ERPs, and other software that ease the daily operations of various areas of a company.
Currently, every effective data initiative needs to have an analytical database capable of centralizing data, so that it is accessible for transformations and more complex analyses. The absence of one contributes to the formation of so-called Data Silos, which occur when information is so sparse and focused on each area that organizational-level decision-making is difficult and time-consuming.
We launched an interesting and comprehensive e-book on this subject to help you combat Data Silos within your company. Click here to download: What are data silos and why they are hindering your growth.
02) ELT
ELT is an acronym that stands for, in English, Extract, Load, and Transform. Within data engineering, it is a set of processes that involves the extraction of structured or unstructured data from various sources, followed by loading it into a Data Lakehouse, and then transforming the data into a format that facilitates analysis and its use.
You should also keep in mind that there is another process called ETL, in which the transformation of data occurs before loading. The main difference between ELT and ETL is this: while ETL transforms data before loading it into the Data Warehouse, ELT first loads the raw data and then performs the transformation as needed.
When it comes to Data Engineering, understanding what ELT and ETL are is important. These techniques allow companies to process large volumes of data quickly and efficiently. By loading raw data first, it is possible to utilize the processing capabilities of data warehouses to perform large-scale transformation.
In addition, using ELT allows companies to create a more flexible data model, with a structure that can be easily modified to meet changing analysis and decision-making needs, ensuring greater space for innovation and re-analysis of the process when necessary.
03) Data Pipelines
A data pipeline is an automated process in data engineering that enables the collection, storage, processing, and analysis of data efficiently and reliably.
It’s like a system that transports your company's data from point A to point B, performing various transformation stages along the way (in the case of ETL), or sending raw data directly to the storage system, with a defined update frequency (every hour, every day, every week…).
The importance of a data pipeline for data engineering is that it allows companies to extract valuable insights from their data quickly and efficiently, in addition to enabling the filtering of useful information in real-time. In other words: data is continuously prepared for analysis, ensuring that companies have access to this information whenever they need it.
Another very important point is that having Data Pipelines is essential for implementing predictive analysis and machine learning, as they can also assist in training machine learning models that utilize your data in real-time.
04) Data Cleaning
Data Cleaning is the iterative process that involves identifying, defining, and correcting errors, inconsistencies, and inaccurate entries in a dataset. It is a very important step in preparing data for analysis and use 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 conclusions and insights derived from it are precise and trustworthy.
This is an important process for data engineering since poorly cleaned datasets can lead to incorrect and imprecise conclusions, resulting in wrong business decisions or machine learning models that do not function properly.
Furthermore, large and complex datasets can have errors and inconsistencies that are difficult to detect manually, which is why the use of automated data cleaning tools is increasingly utilized by data engineers to ensure data quality.
05) Data Activation
Data activation is a technique used in data engineering, aimed at using the information stored in a data warehouse or data lakehouse. Essentially, it is the process of transforming data into actionable insights, in other words, into information that can be used to improve efficiency, decision-making, and business outcomes.
This is very important for any company looking to be data-driven, as it is necessary to apply a systematic approach to collect, store, and analyze data to gain valuable and actionable insights for the business. Data activation occurs when data is transformed into useful insights so that well-founded decision-making takes place.
With proper data activation, it is possible to make more precise decisions, optimize processes, and enhance customer experience, increasing the company’s ROI and improving the daily quality of what is done.
Data Engineering is fundamental to launching and maintaining a data initiative that is sustainable for your company. In this article, we brought some key concepts and tools that are essential to have an engineering framework that helps accelerate your strategy and make your company increasingly data-driven.
In summary, Data Warehouse, ELT, Data Pipelines, Automations, 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.
Remember!
1. The Data Warehouse and Data Lakehouses function as the central point where data is stored and managed.
2. ELT is a modern approach to data transformation that helps simplify the creation of data pipelines.
3. Data Pipelines are necessary to collect, transform, and integrate data from various sources, enabling users to gain timely and precise insights.
4. Data Cleaning is the process of correcting errors, removing duplicates, and ensuring the quality control of available data.
5. Data Activation is a process that enables companies to make better-informed decisions, driving results and helping to increase your company's ROI.
With these technologies, organizations can maximize the value of their data and make strategic decisions based on insights more quickly.
Want access to more Data-Driven content? Explore the other posts on our blog by clicking here.
In the data-driven world, there are many roles, areas, subsystems, and processes, all very important for advancing a company's data maturity. Data Engineering is a vital part of this process, as it is responsible for preparing all the necessary infrastructure to launch and maintain your data operation, as well as integrating, managing, and preparing large amounts of information for analysis and the quest for actionable insights.
With that in mind, we have separated 5 concepts of Data Engineering that 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 its sources, understandable and usable. The engineers responsible for this process are tasked with collecting, identifying, storing, processing, and accessing the data, as well as creating data pipelines and presenting this information for scientists and analysts to act upon.
Erathos has prepared a more in-depth article on this topic, which you can access by clicking here: Data Engineering for Startups.
01) Data Warehouse
A data warehouse is responsible for centralizing all a company's data in a single repository. Here, they will be stored, organized, and managed, allowing for efficient analysis to be performed later. It is like an extensive digital library, where you can access the information you need, regardless of its source, and easily, without having to consult various sources or waste time corroborating information with multiple different databases.
The data stored in a data warehouse is usually structured and optimized to ensure that business reporting and analysis is a simple process. This occurs because it is designed to handle large volumes of data and facilitate complex analyses, in addition to integrating data from various sources such as files, spreadsheets, CRMs, ERPs, and other software that ease the daily operations of various areas of a company.
Currently, every effective data initiative needs to have an analytical database capable of centralizing data, so that it is accessible for transformations and more complex analyses. The absence of one contributes to the formation of so-called Data Silos, which occur when information is so sparse and focused on each area that organizational-level decision-making is difficult and time-consuming.
We launched an interesting and comprehensive e-book on this subject to help you combat Data Silos within your company. Click here to download: What are data silos and why they are hindering your growth.
02) ELT
ELT is an acronym that stands for, in English, Extract, Load, and Transform. Within data engineering, it is a set of processes that involves the extraction of structured or unstructured data from various sources, followed by loading it into a Data Lakehouse, and then transforming the data into a format that facilitates analysis and its use.
You should also keep in mind that there is another process called ETL, in which the transformation of data occurs before loading. The main difference between ELT and ETL is this: while ETL transforms data before loading it into the Data Warehouse, ELT first loads the raw data and then performs the transformation as needed.
When it comes to Data Engineering, understanding what ELT and ETL are is important. These techniques allow companies to process large volumes of data quickly and efficiently. By loading raw data first, it is possible to utilize the processing capabilities of data warehouses to perform large-scale transformation.
In addition, using ELT allows companies to create a more flexible data model, with a structure that can be easily modified to meet changing analysis and decision-making needs, ensuring greater space for innovation and re-analysis of the process when necessary.
03) Data Pipelines
A data pipeline is an automated process in data engineering that enables the collection, storage, processing, and analysis of data efficiently and reliably.
It’s like a system that transports your company's data from point A to point B, performing various transformation stages along the way (in the case of ETL), or sending raw data directly to the storage system, with a defined update frequency (every hour, every day, every week…).
The importance of a data pipeline for data engineering is that it allows companies to extract valuable insights from their data quickly and efficiently, in addition to enabling the filtering of useful information in real-time. In other words: data is continuously prepared for analysis, ensuring that companies have access to this information whenever they need it.
Another very important point is that having Data Pipelines is essential for implementing predictive analysis and machine learning, as they can also assist in training machine learning models that utilize your data in real-time.
04) Data Cleaning
Data Cleaning is the iterative process that involves identifying, defining, and correcting errors, inconsistencies, and inaccurate entries in a dataset. It is a very important step in preparing data for analysis and use 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 conclusions and insights derived from it are precise and trustworthy.
This is an important process for data engineering since poorly cleaned datasets can lead to incorrect and imprecise conclusions, resulting in wrong business decisions or machine learning models that do not function properly.
Furthermore, large and complex datasets can have errors and inconsistencies that are difficult to detect manually, which is why the use of automated data cleaning tools is increasingly utilized by data engineers to ensure data quality.
05) Data Activation
Data activation is a technique used in data engineering, aimed at using the information stored in a data warehouse or data lakehouse. Essentially, it is the process of transforming data into actionable insights, in other words, into information that can be used to improve efficiency, decision-making, and business outcomes.
This is very important for any company looking to be data-driven, as it is necessary to apply a systematic approach to collect, store, and analyze data to gain valuable and actionable insights for the business. Data activation occurs when data is transformed into useful insights so that well-founded decision-making takes place.
With proper data activation, it is possible to make more precise decisions, optimize processes, and enhance customer experience, increasing the company’s ROI and improving the daily quality of what is done.
Data Engineering is fundamental to launching and maintaining a data initiative that is sustainable for your company. In this article, we brought some key concepts and tools that are essential to have an engineering framework that helps accelerate your strategy and make your company increasingly data-driven.
In summary, Data Warehouse, ELT, Data Pipelines, Automations, 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.
Remember!
1. The Data Warehouse and Data Lakehouses function as the central point where data is stored and managed.
2. ELT is a modern approach to data transformation that helps simplify the creation of data pipelines.
3. Data Pipelines are necessary to collect, transform, and integrate data from various sources, enabling users to gain timely and precise insights.
4. Data Cleaning is the process of correcting errors, removing duplicates, and ensuring the quality control of available data.
5. Data Activation is a process that enables companies to make better-informed decisions, driving results and helping to increase your company's ROI.
With these technologies, organizations can maximize the value of their data and make strategic decisions based on insights more quickly.
Want access to more Data-Driven content? Explore the other posts on our blog by clicking here.
In the data-driven world, there are many roles, areas, subsystems, and processes, all very important for advancing a company's data maturity. Data Engineering is a vital part of this process, as it is responsible for preparing all the necessary infrastructure to launch and maintain your data operation, as well as integrating, managing, and preparing large amounts of information for analysis and the quest for actionable insights.
With that in mind, we have separated 5 concepts of Data Engineering that 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 its sources, understandable and usable. The engineers responsible for this process are tasked with collecting, identifying, storing, processing, and accessing the data, as well as creating data pipelines and presenting this information for scientists and analysts to act upon.
Erathos has prepared a more in-depth article on this topic, which you can access by clicking here: Data Engineering for Startups.
01) Data Warehouse
A data warehouse is responsible for centralizing all a company's data in a single repository. Here, they will be stored, organized, and managed, allowing for efficient analysis to be performed later. It is like an extensive digital library, where you can access the information you need, regardless of its source, and easily, without having to consult various sources or waste time corroborating information with multiple different databases.
The data stored in a data warehouse is usually structured and optimized to ensure that business reporting and analysis is a simple process. This occurs because it is designed to handle large volumes of data and facilitate complex analyses, in addition to integrating data from various sources such as files, spreadsheets, CRMs, ERPs, and other software that ease the daily operations of various areas of a company.
Currently, every effective data initiative needs to have an analytical database capable of centralizing data, so that it is accessible for transformations and more complex analyses. The absence of one contributes to the formation of so-called Data Silos, which occur when information is so sparse and focused on each area that organizational-level decision-making is difficult and time-consuming.
We launched an interesting and comprehensive e-book on this subject to help you combat Data Silos within your company. Click here to download: What are data silos and why they are hindering your growth.
02) ELT
ELT is an acronym that stands for, in English, Extract, Load, and Transform. Within data engineering, it is a set of processes that involves the extraction of structured or unstructured data from various sources, followed by loading it into a Data Lakehouse, and then transforming the data into a format that facilitates analysis and its use.
You should also keep in mind that there is another process called ETL, in which the transformation of data occurs before loading. The main difference between ELT and ETL is this: while ETL transforms data before loading it into the Data Warehouse, ELT first loads the raw data and then performs the transformation as needed.
When it comes to Data Engineering, understanding what ELT and ETL are is important. These techniques allow companies to process large volumes of data quickly and efficiently. By loading raw data first, it is possible to utilize the processing capabilities of data warehouses to perform large-scale transformation.
In addition, using ELT allows companies to create a more flexible data model, with a structure that can be easily modified to meet changing analysis and decision-making needs, ensuring greater space for innovation and re-analysis of the process when necessary.
03) Data Pipelines
A data pipeline is an automated process in data engineering that enables the collection, storage, processing, and analysis of data efficiently and reliably.
It’s like a system that transports your company's data from point A to point B, performing various transformation stages along the way (in the case of ETL), or sending raw data directly to the storage system, with a defined update frequency (every hour, every day, every week…).
The importance of a data pipeline for data engineering is that it allows companies to extract valuable insights from their data quickly and efficiently, in addition to enabling the filtering of useful information in real-time. In other words: data is continuously prepared for analysis, ensuring that companies have access to this information whenever they need it.
Another very important point is that having Data Pipelines is essential for implementing predictive analysis and machine learning, as they can also assist in training machine learning models that utilize your data in real-time.
04) Data Cleaning
Data Cleaning is the iterative process that involves identifying, defining, and correcting errors, inconsistencies, and inaccurate entries in a dataset. It is a very important step in preparing data for analysis and use 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 conclusions and insights derived from it are precise and trustworthy.
This is an important process for data engineering since poorly cleaned datasets can lead to incorrect and imprecise conclusions, resulting in wrong business decisions or machine learning models that do not function properly.
Furthermore, large and complex datasets can have errors and inconsistencies that are difficult to detect manually, which is why the use of automated data cleaning tools is increasingly utilized by data engineers to ensure data quality.
05) Data Activation
Data activation is a technique used in data engineering, aimed at using the information stored in a data warehouse or data lakehouse. Essentially, it is the process of transforming data into actionable insights, in other words, into information that can be used to improve efficiency, decision-making, and business outcomes.
This is very important for any company looking to be data-driven, as it is necessary to apply a systematic approach to collect, store, and analyze data to gain valuable and actionable insights for the business. Data activation occurs when data is transformed into useful insights so that well-founded decision-making takes place.
With proper data activation, it is possible to make more precise decisions, optimize processes, and enhance customer experience, increasing the company’s ROI and improving the daily quality of what is done.
Data Engineering is fundamental to launching and maintaining a data initiative that is sustainable for your company. In this article, we brought some key concepts and tools that are essential to have an engineering framework that helps accelerate your strategy and make your company increasingly data-driven.
In summary, Data Warehouse, ELT, Data Pipelines, Automations, 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.
Remember!
1. The Data Warehouse and Data Lakehouses function as the central point where data is stored and managed.
2. ELT is a modern approach to data transformation that helps simplify the creation of data pipelines.
3. Data Pipelines are necessary to collect, transform, and integrate data from various sources, enabling users to gain timely and precise insights.
4. Data Cleaning is the process of correcting errors, removing duplicates, and ensuring the quality control of available data.
5. Data Activation is a process that enables companies to make better-informed decisions, driving results and helping to increase your company's ROI.
With these technologies, organizations can maximize the value of their data and make strategic decisions based on insights more quickly.
Want access to more Data-Driven content? Explore the other posts on our blog by clicking here.