Why Data Engineering Matters
Simplifying the Complex
Many books and articles talk about data engineering and different tools, frameworks, languages you can use. But not many discuss the 'why' of this discipline. Why do we engage in data engineering? What benefits does it offer businesses?
In this piece, I aim to provide simple answers to these questions in a straightforward way.
To really get data engineering, we need to strip it down to its basic parts and build our understanding from the ground up. Let's go through this one step at a time.
The Value of Data
Data engineering focuses on working with data. But first, let's clarify what data is. Data is facts or information, usually in a form computers can understand. This could be numbers, text, or even media files.
In our world, data is like the raw ingredients used by organisations and people to make smart choices, spot trends, get better at what they do, and spark new ideas.
But why is data so important? When we process and analyse data the right way, it gives us insights. These insights can improve decision-making, boost efficiency, make customers happier, and open up new chances for businesses.
Data is valuable because it can be turned into clear insights that greatly influence decisions and results.
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The Role of Data Engineering
As data gets bigger, faster, and more diverse—what some call the three Vs of big data—it's tougher to handle, work with, and get value from. Some data issues include being messy, incomplete, inconsistent, and scattered across different systems.
To deal with and use data well, given its complexity and the variety of sources, we need a planned and scalable way to approach it.
This is where data engineering steps in. It's all about applying practical methods to gather, store, manage, and process data. It lays the groundwork for analysing data and for data science.
Data engineering sets up the systems and methods that allow data to be changed into a format good for analysis. These methods make sure data is correct, easy to get to, and safe.
Data Engineering and Business Value
At its heart, data engineering isn't just about tech skills; it's about helping organisations use their data well. By making sure data is trustworthy, easy to access, and well-organised, data engineering supports data analysts, data scientists, and leaders to get valuable insights from data.
Data engineering is a key base that supports making choices based on data, which can boost performance, spark new ideas, and give a competitive edge. Sometimes, getting the right information early lets us act quickly, which can mean getting ahead of our competitors and grabbing the benefits first. That's why getting the right data early can be such a big advantage.
Data Engineering Processes
These methods make sure that data isn't just gathered but also prepared for analysis in a way that brings out its best value. Let's look at some key parts of data engineering, with examples and good approaches for each.
Data ingestion is about collecting and bringing in data to use right away or store in a database. It means pulling data from different places into one spot, like a data warehouse or data lake.
Some Data ingestion examples:
Pulling in streaming data from social media to analyse feelings in real time.
Gathering sales data at the end of each day from different sales systems.
Good practices for data ingestion:
Pick the Right Tools: Use the best tools for your data type (batch or real time), like Apache NiFi, Apache Kafka, or AWS Kinesis.
Handle Errors Well: Set up strong ways to deal with and fix errors to keep data correct.
Plan for Growth: Make sure your data collection methods can grow to handle more data as needed.
Data storage is about keeping data organised so it can be easily reached and used. What kind of storage you choose depends on the type of data, how much there is, and how you'll access it.
Data storage examples:
Storing raw, unorganised data in a data lake like Amazon S3.
Using a data warehouse like Google BigQuery or Snowflake for organised, processed data.
Best practices for Data storage:
Data Modeling: Use the right data modelling methods to make sure data is stored well and can be quickly found.
Data Partitioning and Indexing: Use partitioning and indexing to speed up searches.
Storage Optimization: Keep an eye on and fine-tune storage costs and efficiency, especially when using cloud services.
Data processing is about changing, cleaning, and improving data to prepare it for analysis. This includes steps like making data consistent, removing duplicates, and changing its format.
Here are some data processing examples:
Cleaning a customer info dataset by getting rid of duplicates and fixing mistakes.
Changing raw log data into a structured format for analysis.
Best practices when processing the data are:
Automation: Use automation for data processing tasks to cut down on manual mistakes and increase efficiency.
Data Quality Checks: Put in place thorough checks for data quality to make sure the processed data is reliable.
Modular Design: Create processing steps that are modular and can be used again for different types of data and situations.
Data orchestration is about arranging and watching over various data processing tasks and their workflows. It makes sure that tasks are done in the right sequence, managing how data moves and depends on these tasks.
Examples of data orchestration:
Setting up a process where data is first brought in, then cleaned, transformed, and finally put into a data warehouse.
Handling a complex process that starts with collecting data from various sources, then processing it, and sending it out to different tools for analysis and display.
Some of best practices for data orchestration:
Workflow Management Tools: Use tools like Apache Airflow, Luigi, or AWS Step Functions to create, plan, and keep an eye on data workflows.
Monitoring and Logging: Set up strong monitoring and logging to quickly spot and fix any issues or slow-downs.
Documentation: Keep detailed records of the orchestration processes and each part to make maintenance and problem-solving easier.
Conclusion
Hopefully this explanation has helped you to understand the “why” of Data engineering. By getting the basics of data engineering, you have learned not just how it's done but also why it's important. It's about making systems and processes that turn raw data into useful insights, crucial in our data-heavy world. When you get into data engineering, keep in mind you're creating important systems that allow data to really make a difference for businesses and companies.
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