What Is Data Engineering in Python?
What Is Data Engineering? Common Challenges and Solutions in Python
January 19, 2023 15:47 PM
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What Is Data Engineering in Python?
January 19, 2023 15:47 PM
Big data. Cloud data. AI training data and personal identifying data. Data is everywhere and growing each day. So it's only natural to say that software engineering developed into data engineering, a field focused on the movement data, storage, and transformation of data.
Maybe you've seen big job ads for data and are fascinated by the possibility of working with petabyte-sized data. Perhaps you're interested in learning how generative adversarial networks generate realistic data images. You may not have had a clue about data engineering; however, you're interested in understanding how software developers manage the massive quantities of data needed to run most applications today.
Whichever class you belong to, this informative article is perfect for you. It will give you a general description of the field, including the definition of data engineering and the work required.
It is a vast field that has many names. In many companies, there is a chance that it isn't a distinct title. Due to this, it's best to determine the objectives of data engineering and then examine the work that will result in the desired results.
The main objective in data engineering is to create a well-organized, consistent flow of data to facilitate data-driven work like:
• Training machine learning services models
• Doing exploratory data analysis
• Fields that are populating in an application using external data
The data flow is done in many ways, and the techniques, tools, and abilities required differ widely between teams, companies, and desired outcomes. But, the most common form will be the pipeline of data. It is a system that comprises independent programs that perform different operations on collected or incoming data.
The data pipelines are usually spread over several servers:
The data could come directly from:
• Internet of Things devices
• Vehicle telemetry
• Real estate data feeds
• User activity is normal in an online application
• Any other collection of measurement tools or other collection you could imagine.
By the nature of these sources, the received data can be processed in real-time or in batches.
The pipeline through which data flows is the task of the engineers working on data. Data engineering teams are accountable for the creation, development, maintenance, expansion, and, often, the infrastructure supporting data pipelines. They could also be accountable for incoming data or, more frequently, the data model and the way data is ultimately stored.
If you think of Data Pipelines as a kind of software, the data engineering field begins to resemble another engineering discipline in software.
A lot of teams are moving towards creating data platforms. In many companies, it's not enough to just have one pipeline for saving any data that comes into an SQL database somewhere. Large companies have several teams that require various levels of accessibility to various types of information.
For instance, artificial intelligence (AI) teams might require methods to label and divide clean data. The business intelligence (BI) teams might require access to data that is easy to aggregate and develop data visualizations. Finally, data science teams could require access to the database level to effectively analyze the data.
If you're familiar with web development, you might find this structure similar to the Model-View-Controller (MVC) design pattern. In MVC, data engineers manage modeling, AI or BI teams are responsible for the views, and all teams collaborate with the controller. Creating data platforms that meet these requirements is the top priority for companies with various teams that depend on access to data.
Once you've witnessed a few things data engineers are doing and how connected their work is with their clients, it's beneficial to know a little more about their customers and their obligations to data engineers.
Now having a better comprehension of the concept of data engineering and its underlying principles, we can dive deeper into understanding the common tasks of data engineering and fields of action pipelines for data, etc.
The initial step in the lifecycle of a project in data engineering--data ingestion involves moving data from various sources into a particular storage or database, where it is used to perform data transformations and analysis.
Storage, too, is worthy of mention because the primary goal for data engineering lies in connecting to various types of storage, extracting data from it, and then saving it for later use.
The issue is that data is available in various file formats, like tab-separated, comma, JSON, and column-oriented such as Parquet and ORC files. Therefore, data engineers frequently confront both unstructured and structured data.
In addition, this data could be stored in different SQL or NoSQL databases, as also data lakes or they may require scraping data from streaming APIs, services, etc.
The name implies that data transformation is changing information from one form to another format. Most of the time, the information that is collected requires an adjustment so that it is in line with the standard of the system's architecture.
In the process of transformation, an engineer in data will conduct the process of normalizing and cleaning data to make the data more easily accessible to users. This may include removing or altering in error, duplicate, corrupted, or missing data from an array of data, casting identical data into one type, and ensuring that dates are the same format as others.
Since all these transformations take place on massive quantities of data, there is a requirement for parallel computation.
The last step is data orchestration--combining and organizing siloed data from various storage locations and making it available for data analysis--as data pipelines comprise several elements: data sources, transformations, and data sinks/targets.
Therefore, data pipelines are constructed from smaller, distinct pieces that use different technologies instead of writing them as a huge block of code.
In addition, the majority of modern tasks for data engineering are done on the cloud. Thus, an engineer in data will require tools that can work effectively in conjunction with cloud computing.
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