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Journey to become a Data Engineer

Quick Intro

Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale. It is a broad field with applications in just about every industry. Organizations have the ability to collect massive amounts of data, and they need the right people and technology to ensure it is in a highly usable state by the time it reaches data scientists and analysts.

In addition to making the lives of data scientists easier, working as a data engineer can give you the opportunity to make a tangible difference in a world where we’ll be producing 463 exabytes per day in 2025. That’s one and 18 zeros of bytes worth of data. Fields like machine learning and deep learning can’t succeed without data engineers to process and channel that data.

What do Data Engineers do?

Data engineers work in a variety of settings to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Their ultimate goal is to make data accessible so that organizations can use it to evaluate and optimize their performance.

These are some common tasks you might perform when working with data:

  • Acquire datasets that align with business needs

  • Develop algorithms to transform data into useful, actionable information

  • Build, test, and maintain database pipeline architectures

  • Collaborate with management to understand company objectives

  • Create new data validation methods and data analysis tools

  • Ensure compliance with data governance and security policies

Working at smaller companies often means taking on a greater variety of data-related tasks in a generalist role. Some bigger companies have data engineers dedicated to building data pipelines and others focused on managing data warehouses—both populating warehouses with data and creating table schemas to keep track of where data is stored.

Skills required for Data Engineering

  • Coding: Proficiency in coding languages is essential to this role, so consider taking courses to learn and practice your skills. Common programming languages include SQL, NoSQL, Python, Java, R, and Scala.

  • Relational and non-relational databases: Databases rank among the most common solutions for data storage. You should be familiar with both relational and non-relational databases, and how they work.

  • ETL (extract, transform, and load) systems: ETL is the process by which you’ll move data from databases and other sources into a single repository, like a data warehouse. Common ETL tools include Xplenty, Stitch, Alooma, and Talend.

  • Data storage: Not all types of data should be stored the same way, especially when it comes to big data. As you design data solutions for a company, you’ll want to know when to use a data lake versus a data warehouse, for example.

  • Automation and scripting: Automation is a necessary part of working with big data simply because organizations are able to collect so much information. You should be able to write scripts to automate repetitive tasks.

  • Machine learning: While machine learning is more the concern of data scientists, it can be helpful to have a grasp of the basic concepts to better understand the needs of data scientists on your team.

  • Big data tools: Data engineers don’t just work with regular data. They’re often tasked with managing big data. Tools and technologies are evolving and vary by company, but some popular ones include Hadoop, MongoDB, and Kafka.

  • Cloud computing: You’ll need to understand cloud storage and cloud computing as companies increasingly trade physical servers for cloud services. Beginners may consider a course in Amazon Web Services (AWS) or Google Cloud.

  • Data security: While some companies might have dedicated data security teams, many data engineers are still tasked with securely managing and storing data to protect it from loss or theft.

Data Pipeline

A Data pipeline is basically a set of tools and processes for moving data from one system to another for storage and further handling. It captures datasets from multiple sources and inserts them into some form of database, another tool, or app, providing quick and reliable access to this combined data for the teams of data scientistsBI engineers, data analysts, etc.

Constructing data pipelines is the core responsibility of data engineering. It requires advanced programming skills to design a program for continuous and automated data exchange. A data pipeline is commonly used for

  • moving data to the cloud or to a data warehouse,

  • wrangling the data into a single location for convenience,

  • integrating data from various connected devices and systems in IoT,

  • copying databases into a cloud data warehouse, and

  • bringing data to one place in BI for informed business decisions.

What does ETL stand for and mean?

  1. Extract – At the start of the pipeline we deal with raw data, which can be retrieved from numerous sources. Data engineers run ‘jobs’ to extract this data.

  2. Transform – Data engineers run jobs to transform the data into an acceptable format for further processing or standardisation of data in the system. It is a very important step in the process as it increases data usability.

  3. Load – A step which saves data to a new location, possibly into a RDMS (Relational Data Management System)

Data Warehouse

A data warehouse is a central repository where data is stored in query-able forms. From a technical standpoint, a data warehouse is a relational database optimized for reading, aggregating, and querying large volumes of data. Traditionally, DWs only contained structured data, or data that can be arranged in tables.

Data Mart

A data mart is a smaller scale data warehouse, it is usually smaller than 100GBs

Data Lake

A data lake is a vast pool for saving data in its natural unprocessed. A data lake stands out for its high speed and agility as it is not limited to the Data Warehouse’s configuration

Hadoop

Hadoop is a large-scale data processing framework on Java. The platform allows for splitting data analysis jobs across various computers and processing them in parallel.

Ingest

You may acquire raw data in a variety of ways, depending on the amount, source, and latency of the data.

  • Data from app events, such as log files or user events, usually gathers via a push paradigm, in which the app uses an API to deliver the data to storage.

  • Streaming: The data is a series of short, asynchronous messages that are in a continuous stream.

  • Batch: A series of files containing a large amount of data is sent to storage in bulk.

Store

Data arrives in a variety of shapes and sizes, and its structure determines by the sources from which it derives. And, also the downstream use cases. Ingest data can store in a number of forms or places for data and analytics applications.

Process and Analyse

You must convert and analyze data in order to gain business value and insights from it. This necessitates a processing architecture that can either analyze the data directly or prepare it for downstream analysis, as well as tools to assess and comprehend the outcomes of the processing.

  • Processing: Data from source systems is cleansed, normalized, and processed across multiple machines, and stored in analytical systems.

  • Analysis: Processed data stores in systems that allow for ad-hoc querying and exploration.

  • Understanding: Depending on analytical results, data can be train and test automated machine-learning models.

Explore and Visualise

In-depth data exploration and visualization are the final steps in the data lifecycle, and they help you better comprehend the outcomes of your processing and analysis. Insights operate improvements in the pace or amount of data input, the use of various storage mediums to expedite analysis, and upgrades to processing pipelines during exploration. Data scientists and business analysts having skills in probability, statistics, and recognizing business value, frequently explore and analyze big data sets.

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