Data Engineering Paradigm

Data engineering involves designing a system that handles the collection of raw “source” data, transforms it according to the initial business use case or use cases, and finally to production data. Data Engineering, at its core, is concerned with transforming raw data into an understandable state so that other applications and platforms can consume it.

Data engineering is a new type of engineering focusing on managing and scaling data. All over the world, there is a need to run sophisticated data processing – all kinds of things from fraud detection, ad optimization, churn prediction, to web page design. The tools and technologies are ready to help you scale big data processing.

Data Engineers play a crucial role in advancing the field of data science. They extract value from data not typically accessible to typical analysis. They are coming up with new ideas and methodologies for models to be built on top of large amounts of data. Yet despite their importance, education in data engineering has been limited…until now.

It’s not straightforward. Before a company can optimize the business more efficiently or build data products more intelligently, layers of foundational work need to be made first. And when it’s done incorrectly or left to grow out of control, companies and teams fall subject to the dreaded “analysis paralysis.” The cure is found in data engineering.

Before we get into more advanced applications of AI, a company needs to lay the foundation. The layers underneath include collecting, labeling, and cleaning data. For example, if data collection efforts are not thorough, AI won’t work well because it doesn’t have the information required to achieve the organization’s goal.

Without that work, companies scramble to make progress using outdated tools and practices. So many companies need months to set up basic infrastructure in their data organization. Many of the required capabilities are impossible to implement without modern technologies like AI Surge. Too much time is being spent maintaining existing systems instead of improving them or building new ones. The operations burden is increasing complexity and decreasing business agility and productivity.

To do anything with data in a system, you must first ensure that it can reliably flow into and through the system. With AI Surge’s No Code Connector, you can connect to all your data sources and start working on your data in minutes.

The significant advantage of Data Engineering over traditional modeling techniques is that it relies heavily on metadata to provide extensive knowledge of the underlying database. Metadata is essentially a description of a data model, and Data Engineering attempts to define the most appropriate metadata needed for each model.

The ultimate goal of data engineering is to provide organized and consistent data flow to enable data-driven work in the organization so that business users can perform their day-to-day roles with minimal friction and distractions. When done right, this results in a well-tuned pipeline for delivering new data to other teams across the company.

In an entirely data-driven organization, it’s easier to make decisions. Everyone can access the same up-to-date information. Business users and analysts have clean, understandable data to build new models, create new products, and innovate with data.

Data engineering is a growing business domain that supports most other branches of data science. Data processing is the crucial link between data collection and data analysis. Most analytics software and tools are designed with this focus on batch processing. However, this is not practical for many real-time or nearly real-time applications where we need streaming analytics. Streaming analytics can process streaming data in motion to generate results as fast as possible while keeping storage and bandwidth constraints in mind.

Data Normalization and Modeling

Data flowing into a system is excellent. However, the data need to conform to some architectural standard at some point. Normalizing data is a process that serves to improve the quality of the data being used by an application. This includes but is not limited to the following steps:

  • Removing duplicates (deduplication)

  • Removing duplicate records or conflicting values from distinct databases- Data Profiling

  • Fixing conflicting data

  • Conforming data to a specified data model

  • Synchronizing data from different sources

Data Cleaning

Data can be messy. If you’re storing data in a database, it’ll need some clean-up before you can start using it.

Data cleaning is cleaning collected data to be classified into a desired form for analysis. There are many different ways that you can clean your data. In general, you want your data to be as accurate, complete, and consistent as possible for analysis. Examples of Cleaning Data: Casting the same data to a single type (for example, forcing strings in an integer field to be integers) Removing undesirable values and undesired cases (pattern) Removing empty or duplicated variables Eliminating errors and inconsistencies Ensuring data has been cleaned – Checking error lists

If you’ve ever worked with data from multiple sources, you might be familiar with the problem of data that differs from what you expected. One commonly problematic piece is when data comes in as a string (text) but needs to be an integer. For example, if you’re working with a list of user IDs and want to run a report on them, you might find some users were input as text (hope I got IDs everywhere!) and others as integers or even double-digit numbers. You might need to cast all those strings into an integer type to perform any operations (summing, grouping, etc.) or transformations on them. This is typically called casting in the software engineering world – meaning “force

It’s tempting to skip data cleaning when you want to build a beautiful dashboard on your database. However, the outcome is always messy, and inaccurate data flows. What if there is already data in your database which takes a specific type of data? Well, you know how frustrating and difficult it can be for anyone who has ever tried to do data science on a database with mixed values.

Data Accessibility

Data accessibility refers to how easy the data is for customers to access and understand. Data accessibility is somewhat of a subjective term, meaning how accessible the data vary from customer to customer. For example, a business person will need to access his company’s product shipments every day on a schedule. The company’s CEO may only need monthly reports regarding which products are doing well in what markets.

Data accessibility is a somewhat ambiguous term that refers to the ability to use data for analysis and application. There are three principal means to improve data accessibility:

  1. data storage – This could be either cloud or on-premises.

  2. Access to data – Whether one will use other programs, analytics, databases, or even the command line, or query the data from within their tools.

  3. Visualizability – Whether some interface allows them to create visualizations and reports.

Data accessibility is key to the success of A.I. and any businesses that want to leverage data for insights. Whenever I think about data accessibility, I think of an image from the “House of Cards” series. In the first episode, when Frank Underwood was still a congressman, he stood on a balcony with a couple of other people watching a computer database in the distance. He said, “We are thinking about things too small and too far away; we have lost sight of the big picture.”When we lose sight of the big picture, we lose sight of the most important – human well-being.

Data engineering involves designing a system that handles the collection of raw “source” data, transforms it according to the initial business use case or use cases, and finally to production data. Data Engineering, at its core, is concerned with transforming raw data into an understandable state so that other applications and platforms can consume it.

Data engineering is a new type of engineering focusing on managing and scaling data. All over the world, there is a need to run sophisticated data processing – all kinds of things from fraud detection, ad optimization, churn prediction, to web page design. The tools and technologies are ready to help you scale big data processing.

Data Engineers play a crucial role in advancing the field of data science. They extract value from data not typically accessible to typical analysis. They are coming up with new ideas and methodologies for models to be built on top of large amounts of data. Yet despite their importance, education in data engineering has been limited…until now.

It’s not straightforward. Before a company can optimize the business more efficiently or build data products more intelligently, layers of foundational work need to be made first. And when it’s done incorrectly or left to grow out of control, companies and teams fall subject to the dreaded “analysis paralysis.” The cure is found in data engineering.

Before we get into more advanced applications of AI, a company needs to lay the foundation. The layers underneath include collecting, labeling, and cleaning data. For example, if data collection efforts are not thorough, AI won’t work well because it doesn’t have the information required to achieve the organization’s goal.

Without that work, companies scramble to make progress using outdated tools and practices. So many companies need months to set up basic infrastructure in their data organization. Many of the required capabilities are impossible to implement without modern technologies like AI Surge. Too much time is being spent maintaining existing systems instead of improving them or building new ones. The operations burden is increasing complexity and decreasing business agility and productivity.

To do anything with data in a system, you must first ensure that it can reliably flow into and through the system. With AI Surge’s No Code Connector, you can connect to all your data sources and start working on your data in minutes.

The significant advantage of Data Engineering over traditional modeling techniques is that it relies heavily on metadata to provide extensive knowledge of the underlying database. Metadata is essentially a description of a data model, and Data Engineering attempts to define the most appropriate metadata needed for each model.

The ultimate goal of data engineering is to provide organized and consistent data flow to enable data-driven work in the organization so that business users can perform their day-to-day roles with minimal friction and distractions. When done right, this results in a well-tuned pipeline for delivering new data to other teams across the company.

In an entirely data-driven organization, it’s easier to make decisions. Everyone can access the same up-to-date information. Business users and analysts have clean, understandable data to build new models, create new products, and innovate with data.

Data engineering is a growing business domain that supports most other branches of data science. Data processing is the crucial link between data collection and data analysis. Most analytics software and tools are designed with this focus on batch processing. However, this is not practical for many real-time or nearly real-time applications where we need streaming analytics. Streaming analytics can process streaming data in motion to generate results as fast as possible while keeping storage and bandwidth constraints in mind.

Data Normalization and Modeling

Data flowing into a system is excellent. However, the data need to conform to some architectural standard at some point. Normalizing data is a process that serves to improve the quality of the data being used by an application. This includes but is not limited to the following steps:

  • Removing duplicates (deduplication)

  • Removing duplicate records or conflicting values from distinct databases- Data Profiling

  • Fixing conflicting data

  • Conforming data to a specified data model

  • Synchronizing data from different sources

Data Cleaning

Data can be messy. If you’re storing data in a database, it’ll need some clean-up before you can start using it.

Data cleaning is cleaning collected data to be classified into a desired form for analysis. There are many different ways that you can clean your data. In general, you want your data to be as accurate, complete, and consistent as possible for analysis. Examples of Cleaning Data: Casting the same data to a single type (for example, forcing strings in an integer field to be integers) Removing undesirable values and undesired cases (pattern) Removing empty or duplicated variables Eliminating errors and inconsistencies Ensuring data has been cleaned – Checking error lists

If you’ve ever worked with data from multiple sources, you might be familiar with the problem of data that differs from what you expected. One commonly problematic piece is when data comes in as a string (text) but needs to be an integer. For example, if you’re working with a list of user IDs and want to run a report on them, you might find some users were input as text (hope I got IDs everywhere!) and others as integers or even double-digit numbers. You might need to cast all those strings into an integer type to perform any operations (summing, grouping, etc.) or transformations on them. This is typically called casting in the software engineering world – meaning “force

It’s tempting to skip data cleaning when you want to build a beautiful dashboard on your database. However, the outcome is always messy, and inaccurate data flows. What if there is already data in your database which takes a specific type of data? Well, you know how frustrating and difficult it can be for anyone who has ever tried to do data science on a database with mixed values.

Data Accessibility

Data accessibility refers to how easy the data is for customers to access and understand. Data accessibility is somewhat of a subjective term, meaning how accessible the data vary from customer to customer. For example, a business person will need to access his company’s product shipments every day on a schedule. The company’s CEO may only need monthly reports regarding which products are doing well in what markets.

Data accessibility is a somewhat ambiguous term that refers to the ability to use data for analysis and application. There are three principal means to improve data accessibility:

  1. data storage – This could be either cloud or on-premises.

  2. Access to data – Whether one will use other programs, analytics, databases, or even the command line, or query the data from within their tools.

  3. Visualizability – Whether some interface allows them to create visualizations and reports.

Data accessibility is key to the success of A.I. and any businesses that want to leverage data for insights. Whenever I think about data accessibility, I think of an image from the “House of Cards” series. In the first episode, when Frank Underwood was still a congressman, he stood on a balcony with a couple of other people watching a computer database in the distance. He said, “We are thinking about things too small and too far away; we have lost sight of the big picture.”When we lose sight of the big picture, we lose sight of the most important – human well-being.

Data engineering involves designing a system that handles the collection of raw “source” data, transforms it according to the initial business use case or use cases, and finally to production data. Data Engineering, at its core, is concerned with transforming raw data into an understandable state so that other applications and platforms can consume it.

Data engineering is a new type of engineering focusing on managing and scaling data. All over the world, there is a need to run sophisticated data processing – all kinds of things from fraud detection, ad optimization, churn prediction, to web page design. The tools and technologies are ready to help you scale big data processing.

Data Engineers play a crucial role in advancing the field of data science. They extract value from data not typically accessible to typical analysis. They are coming up with new ideas and methodologies for models to be built on top of large amounts of data. Yet despite their importance, education in data engineering has been limited…until now.

It’s not straightforward. Before a company can optimize the business more efficiently or build data products more intelligently, layers of foundational work need to be made first. And when it’s done incorrectly or left to grow out of control, companies and teams fall subject to the dreaded “analysis paralysis.” The cure is found in data engineering.

Before we get into more advanced applications of AI, a company needs to lay the foundation. The layers underneath include collecting, labeling, and cleaning data. For example, if data collection efforts are not thorough, AI won’t work well because it doesn’t have the information required to achieve the organization’s goal.

Without that work, companies scramble to make progress using outdated tools and practices. So many companies need months to set up basic infrastructure in their data organization. Many of the required capabilities are impossible to implement without modern technologies like AI Surge. Too much time is being spent maintaining existing systems instead of improving them or building new ones. The operations burden is increasing complexity and decreasing business agility and productivity.

To do anything with data in a system, you must first ensure that it can reliably flow into and through the system. With AI Surge’s No Code Connector, you can connect to all your data sources and start working on your data in minutes.

The significant advantage of Data Engineering over traditional modeling techniques is that it relies heavily on metadata to provide extensive knowledge of the underlying database. Metadata is essentially a description of a data model, and Data Engineering attempts to define the most appropriate metadata needed for each model.

The ultimate goal of data engineering is to provide organized and consistent data flow to enable data-driven work in the organization so that business users can perform their day-to-day roles with minimal friction and distractions. When done right, this results in a well-tuned pipeline for delivering new data to other teams across the company.

In an entirely data-driven organization, it’s easier to make decisions. Everyone can access the same up-to-date information. Business users and analysts have clean, understandable data to build new models, create new products, and innovate with data.

Data engineering is a growing business domain that supports most other branches of data science. Data processing is the crucial link between data collection and data analysis. Most analytics software and tools are designed with this focus on batch processing. However, this is not practical for many real-time or nearly real-time applications where we need streaming analytics. Streaming analytics can process streaming data in motion to generate results as fast as possible while keeping storage and bandwidth constraints in mind.

Data Normalization and Modeling

Data flowing into a system is excellent. However, the data need to conform to some architectural standard at some point. Normalizing data is a process that serves to improve the quality of the data being used by an application. This includes but is not limited to the following steps:

  • Removing duplicates (deduplication)

  • Removing duplicate records or conflicting values from distinct databases- Data Profiling

  • Fixing conflicting data

  • Conforming data to a specified data model

  • Synchronizing data from different sources

Data Cleaning

Data can be messy. If you’re storing data in a database, it’ll need some clean-up before you can start using it.

Data cleaning is cleaning collected data to be classified into a desired form for analysis. There are many different ways that you can clean your data. In general, you want your data to be as accurate, complete, and consistent as possible for analysis. Examples of Cleaning Data: Casting the same data to a single type (for example, forcing strings in an integer field to be integers) Removing undesirable values and undesired cases (pattern) Removing empty or duplicated variables Eliminating errors and inconsistencies Ensuring data has been cleaned – Checking error lists

If you’ve ever worked with data from multiple sources, you might be familiar with the problem of data that differs from what you expected. One commonly problematic piece is when data comes in as a string (text) but needs to be an integer. For example, if you’re working with a list of user IDs and want to run a report on them, you might find some users were input as text (hope I got IDs everywhere!) and others as integers or even double-digit numbers. You might need to cast all those strings into an integer type to perform any operations (summing, grouping, etc.) or transformations on them. This is typically called casting in the software engineering world – meaning “force

It’s tempting to skip data cleaning when you want to build a beautiful dashboard on your database. However, the outcome is always messy, and inaccurate data flows. What if there is already data in your database which takes a specific type of data? Well, you know how frustrating and difficult it can be for anyone who has ever tried to do data science on a database with mixed values.

Data Accessibility

Data accessibility refers to how easy the data is for customers to access and understand. Data accessibility is somewhat of a subjective term, meaning how accessible the data vary from customer to customer. For example, a business person will need to access his company’s product shipments every day on a schedule. The company’s CEO may only need monthly reports regarding which products are doing well in what markets.

Data accessibility is a somewhat ambiguous term that refers to the ability to use data for analysis and application. There are three principal means to improve data accessibility:

  1. data storage – This could be either cloud or on-premises.

  2. Access to data – Whether one will use other programs, analytics, databases, or even the command line, or query the data from within their tools.

  3. Visualizability – Whether some interface allows them to create visualizations and reports.

Data accessibility is key to the success of A.I. and any businesses that want to leverage data for insights. Whenever I think about data accessibility, I think of an image from the “House of Cards” series. In the first episode, when Frank Underwood was still a congressman, he stood on a balcony with a couple of other people watching a computer database in the distance. He said, “We are thinking about things too small and too far away; we have lost sight of the big picture.”When we lose sight of the big picture, we lose sight of the most important – human well-being.