Understanding data engineering

What is data engineering and why do we need it?

Posted by Allan Situma on February 27, 2020 · 7 mins read

Introduction

Engineers like to solve problems. If there are no problems handily available, they will create their own problems. - Scott Adams

Data science as a professional is currently getting much of the hype in the data world.However not much is being said about the enablers of data science, i.e. data engineers. In the real world, there is need for professionals who find data from different data sources, store, clean and aggregate this data ready for use by data scientist and othere end users

In this articles, we will talk about what data engineering is about and also the tools and frameworks used. We will finally touch on a subset of data engineering i.e. analytics engineering

Agenda

  • What is data engineering
  • Data engineering stages
  • Tools used by data engineers
  • Serverless data engineering

Data Engineering Overview

Data engineering is a field within data science and analytics that focuses on designing, building, and maintaining the infrastructure necessary for data processing and analysis. Data engineers work with large volumes of data, ensuring its availability, reliability, and efficiency for use by data scientists, analysts, and other stakeholders within an organization. They are responsible for developing and maintaining data pipelines, databases, and data warehouses, among other tasks.

Data Engineering Stages

Data engineering typically involves several stages:

  1. Data Ingestion: Acquiring data from various sources such as databases, files, APIs, streaming platforms, and sensors.
  2. Data Storage: Storing the acquired data in appropriate formats and structures, often in data lakes, data warehouses, or databases.
  3. Data Processing: Cleaning, transforming, and enriching the data to make it suitable for analysis. This may involve tasks such as deduplication, normalization, aggregation, and joining datasets.
  4. Data Modeling: Designing and implementing data models to organize and structure the data for efficient querying and analysis. This includes schema design, indexing, and optimization.
  5. Data Quality and Governance: Ensuring data quality and compliance with regulations and organizational standards. This involves implementing data validation, monitoring, and auditing processes.
  6. Data Integration: Integrating data from multiple sources to create a unified view of the data. This may involve data consolidation, synchronization, and federation.
  7. Data Delivery: Delivering the processed data to downstream applications, analytics platforms, and end-users in a timely and efficient manner.

Tools Used by Data Engineers

Data engineers use a variety of tools and technologies to perform their tasks efficiently. Some common tools include:

  1. Apache Hadoop: A distributed storage and processing framework for handling large datasets across clusters of computers.
  2. Apache Spark: A fast and general-purpose cluster computing system for processing large-scale data.
  3. Apache Kafka: A distributed streaming platform for building real-time data pipelines and streaming applications.
  4. Apache Airflow: A platform for programmatically authoring, scheduling, and monitoring workflows and data pipelines.
  5. SQL Databases: Relational databases such as PostgreSQL, MySQL, and SQL Server are commonly used for storing structured data.
  6. NoSQL Databases: Non-relational databases like MongoDB, Cassandra, and Redis are used for storing semi-structured and unstructured data.
  7. ETL Tools: Extract, Transform, Load (ETL) tools such as Talend, Informatica, and Apache NiFi are used for data integration and transformation.
  8. Data Warehousing Solutions: Cloud-based data warehousing solutions like Amazon Redshift, Google BigQuery, and Snowflake provide scalable storage and analytics capabilities.
  9. Data Quality Tools: Tools like Trifacta, Talend Data Quality, and Informatica Data Quality are used for data cleansing, profiling, and validation.

Serverless Data Engineering

Serverless data engineering refers to the practice of building and deploying data pipelines and applications without managing the underlying infrastructure. In a serverless architecture, cloud providers handle the infrastructure provisioning, scaling, and maintenance, allowing data engineers to focus on writing code and developing applications.

Serverless Data Engineering Tools

In addition to traditional data engineering tools, serverless data engineering leverages cloud-native platforms and services to streamline infrastructure management and scalability. Here are some key serverless data engineering tools commonly used by data engineers:

  1. AWS Lambda: AWS Lambda is a serverless compute service provided by Amazon Web Services (AWS) that allows you to run code in response to events and automatically scales to handle incoming requests.
  2. Google Cloud Functions: Google Cloud Functions is a serverless execution environment provided by Google Cloud Platform (GCP) that enables you to run code in response to events triggered by GCP services or HTTP requests.
  3. Azure Functions: Azure Functions is a serverless compute service provided by Microsoft Azure that enables you to run event-driven code without having to provision or manage servers.
  4. AWS Glue: AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load data for analytics. It can run serverless Apache Spark jobs to process large volumes of data.
  5. Google Cloud Dataflow: Google Cloud Dataflow is a fully managed stream and batch processing service provided by GCP. It enables you to run serverless Apache Beam pipelines for data processing.
  6. Azure Data Factory: Azure Data Factory is a cloud-based data integration service provided by Microsoft Azure that allows you to create, schedule, and orchestrate data pipelines using a serverless architecture.
  7. AWS Step Functions: AWS Step Functions is a serverless orchestration service provided by AWS that enables you to coordinate the execution of multiple AWS Lambda functions and other AWS services in a visual workflow.
  8. Google Cloud Composer: Google Cloud Composer is a managed Apache Airflow service provided by GCP that allows you to create, schedule, and monitor workflows for data pipelines and data processing tasks.
  9. Azure Logic Apps: Azure Logic Apps is a cloud-based workflow automation service provided by Microsoft Azure that enables you to automate the integration and orchestration of data and services across cloud and on-premises environments.
  10. Serverless Framework: Serverless Framework is an open-source framework that simplifies the deployment and management of serverless applications across different cloud providers, including AWS, GCP, and Azure.

These serverless data engineering tools and platforms offer benefits such as automatic scaling, reduced operational overhead, and pay-per-use pricing models, making them well-suited for building scalable and cost-effective data pipelines and applications.

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