A data engineer is an IT worker whose primary job is to prepare data for analytical or operational uses. These software engineers are typically responsible for building data pipelines to bring together information from different source systems. They integrate, consolidate and cleanse data and structure it for use in analytics applications. They aim to make data easily accessible and to optimize their organization's big data ecosystem.
Data engineers work in conjunction with data science teams, improving data transparency and enabling businesses to make more trustworthy business decisions.
Data Engineer Role:
Data engineer focus on collecting and preparing data for use by data scientists and analysts. They are three main roles of data engineer as follows:
Data Engineer with general focus work on small teams, doing end-to-end data collection, intake and processing. They have more skills than other data engineer but less knowledge of system architecture.
A project a generalist data engineer might undertake for a small, metro-area food delivery service would be to create a dashboard that displays the number of deliveries made each day for the past month and forecasts the delivery volume for the following month.
2.. Pipeline - Centric Engineer:
These data engineers typically work on a midsize data analytics team and more complicated data science projects across distributed systems. Midsize and large companies are more likely to need this role. A regional food delivery company might undertake a pipeline-centric project to create a tool for data scientists and analysts to search metadata for information about deliveries. They might look at distance driven and drive time required for deliveries in the past month, then use that data in a predictive algorithm to see what it means for the company's future business.
3. Database - Centric Engineer:
These data engineers are tasked with implementing, maintaining and populating analytics databases. This role typically exists at larger companies where data is distributed across several databases. The engineers work with pipelines, tune databases for efficient analysis and create table schemas using extract, transform, load (ETL) methods. ETL is a process in which data is copied from several sources into a single destination system.
Data Engineer Tools
Python is general purpose programming language. Python is called as Army knife due to its multiple use cases in building data pipeline. Data Engineer use python to code ETL frameworks, API interactions, automation and data munging tasks such as reshaping, aggregating, joining disparate source etc.
SQL is used by Data Engineers to create business logic models, execute complex queries, extract key performance metrics and build reusable data structure. SQL is important tool to access, update, insert, manipulate and modify data using different queries, data transformation techniques and more.
PostgreSQL is lightweight, highly flexible, highly capable, and is built using an object-relational model. It offers a wide range of built-in and user-defined functions, extensive data capacity, and trusted data integrity. Specifically designed to work with large datasets while offering high fault tolerance, PostgreSQL makes an ideal choice for data engineering workflows.
MongoDB is a popular NoSQL database. It’s easy-to-use, highly flexible, and can store and query both structured and unstructured data at a high scale. NoSQL databases (such as MongoDB) gained popularity due to their ability to handle unstructured data. Unlike relational databases (SQL) with rigid schemas, NoSQL databases are much more flexible and store data in simple forms that are easy to understand.
5. Apache Spark
Apache Spark, An open-source analytics engine known for its large-scale data processing capabilities, supports multiple programming languages, including Java, Scala, R, and Python. Spark can process terabytes of streams in micro-batches and uses in-memory caching and optimized query execution.
6. Apache Kafka
Apache Kafka is an open-source event streaming platform with multiple applications such as data synchronization, messaging, real-time data streaming, and more. Apache Kafka is popular for building ELT pipelines and is widely used as a data collection and ingestion tool. A simple, reliable, scalable, and high-performance tool, Apache Kafka can stream large amounts of data into a target quickly.
7. Amazon Redshift
Amazon Redshift is an excellent example–it is a fully-managed cloud-based data warehouse designed for large-scale data storage and analysis. Redshift makes it easy to query and combine huge amounts of structured and semi-structured data across data warehouses, operational databases, and data lakes using standard SQL. It also allows data engineers to easily integrate new data sources within hours, which reduces time to insight.
Snowflake is a popular cloud-based data warehousing platform that offers businesses separate storage and compute options, support for third-party tools, data cloning, and much more. Snowflake helps streamline data engineering activities by easily ingesting, transforming, and delivering data for deeper insights. With Snowflake, data engineers do not have to worry about managing infrastructure, concurrency handling, etc., and can focus on other valuable activities for delivering your data.
9. Amazon Athena
Amazon Athena is an interactive query tool that helps you analyze unstructured, semi-structured, and structured data stored in Amazon S3 (Amazon Simple Storage Service). You can use Athena for ad-hoc querying on structured and unstructured data using standard SQL. Athena is completely serverless, which means there’s no need to manage or set up any infrastructure. With Athena, you do not need complex ETL jobs to prepare your data for analysis. This makes it easy for data engineers or anyone with SQL skills to analyze large datasets in no time.
10. Apache Airflow
Apache Airflow has been a favorite tool for data engineers for orchestrating and scheduling their data pipelines. Apache Airflow helps you build modern data pipelines through efficient scheduling of tasks. It offers a rich user interface to easily visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.
Resource: Medium, Whalts
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