Decoding Data Engineering: Streamlining Data for Business Growth
The modern workspace is not just about collecting and storing data but also about processing different types of data from databases into a single repository that can be used by businesses to understand information better. Data engineering achieves this by building and maintaining a cost-effective infrastructure for processing and storing large data volumes.
Why is data engineering important?
The amount of data generated at high speeds presents numerous technical challenges in extracting, storing, managing, and analyzing valuable information that can steer business growth. In order to make sense of the data collected, it's necessary to have a data engineer to navigate through the raw data and provide clear direction.
Data engineering and the ETL process
Data engineering is a versatile field that focuses on both ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes. These methodologies help ensure that data is not only accurately processed and stored, but also remains accessible and secure. Data engineers are responsible for ensuring that the infrastructure and tools necessary for data processing and storage are in place, ensuring the data is accessible, clean, and secure, although data scientists and data analysts can be involved as well.
Data engineers vs data scientists vs data analysts
The roles of data engineers, data scientists, and data analysts are interrelated yet distinct. Data engineers are primarily responsible for establishing and managing the data infrastructure, including both ETL and ELT processes. They play a crucial role in preparing data for analysis by ensuring its accessibility and quality.
Data scientists delve deeper into this prepared data, applying advanced analytics like statistical analysis, predictive modelling, and machine learning. Their involvement often starts in the planning phase of the data pipeline, collaborating with engineers to define data needs and formats.
Data analysts focus on extracting insights and identifying trends from the processed data. While their primary role is not in setting up the ETL processes, they sometimes engage in preliminary data preparation, especially in smaller or more integrated teams. Their expertise lies in translating data findings into actionable business insights and recommendations.
This dynamic reflects the collaborative nature of data roles, where the specific responsibilities can overlap and vary based on the organization's context.
What is the ETL or ELT process?
The ETL or ELT process is an essential aspect of data engineering that involves the collection and refining of data from multiple sources. It is then structured and stored in a way that makes it readily available for business intelligence and decision-making purposes.
The ETL process involves three fundamental steps: Extract, Transform, and Load. Once the data is extracted, it can then be either transformed before loading (ETL) or it can also be loaded first and then transformed within the target system (ELT).
|Order of the Process
|Extract, Transform, Load (ETL)
|Extract, Load, Transform (ELT)
|Transformation before loading
|Data loading and transformation within the warehouse
|Anonymization of sensitive info before load
|Privacy handling within the data warehouse
|Transformation logic, schema management might need manual intervention
|Relies on the data warehouse's capabilities for maintaining transformations. May require careful data quality management within the warehouse
|Higher latency due to transformation (can be minimized via streaming)
|Potentially lower latency, especially with minimal transformation
|Custom rules for handling edge cases
|Generalized approach may require careful data quality management
|Requires predefined models
|Allows flexibility as data and schemas evolve
|Scale of Data
|Scalability might be limited if not scaled properly
|Leverages data warehouse processing power for scalability
Not all data are the same
Data engineering deals with various types of data, namely structured, semi-structured, and unstructured data.
Structured data refers to the data that is organized in a specific format and can be easily processed and analyzed by machines. Examples of structured data include data in spreadsheets or databases.
Semi-structured data, on the other hand, is a combination of structured and unstructured data. It has a defined structure, but the structure may vary from one source to another. Examples of semi-structured data include XML files or JSON documents.
Unstructured data refers to data that has no specific structure or format. Examples of this type of data include social media posts, emails, videos, images, and audio recordings.
The primary objective of the data engineering process is to transform data from multiple sources into a format that can be analyzed and stored in a suitable data storage system. There are three primary types of data storage systems used in data engineering:
- Data Warehouse: A data warehouse is a centralized storage system that integrates data from multiple sources, optimized for query and analysis, and is structured specifically for easy read access. Data warehouses are ideal for handling structured data with a defined schema, facilitating comprehensive reporting and data analysis.
- Data Mart: A data mart is a more focused subset of a data warehouse, catering to specific business areas, departments, or subject areas. It provides a more limited scope and size than a full data warehouse, allowing users access to data needed for their specific functions.
- Data Lake: A data lake is also a storage system, but it accommodates structured, semi-structured, and unstructured data. It's particularly suited for big data analytics scenarios, offering flexibility and the ability to store large volumes of data without defining the data model upfront.
Tools in data engineering
Data engineers employ various tools and technologies to manage and manipulate data effectively, including programming languages like Python and SQL and technologies like Apache Hadoop, Spark, Kafka, and cloud services. Some of the top technologies and tools in building scalable and efficient data processing systems are:
- Apache Hadoop: Hadoop is a popular open-source framework that is used for distributed processing and storage of large data sets. It includes the Hadoop Distributed File System (HDFS) for storage and MapReduce for processing. Hadoop is commonly used for batch processing in ETL pipelines.
- Apache Spark: Spark is another popular distributed computing framework that is used for processing large data sets. It includes APIs for batch processing, streaming, machine learning, and graph processing. Spark is commonly used for real-time processing in ETL pipelines.
- Apache Kafka: Kafka is a distributed streaming platform that is used for handling real-time data feeds. It provides high-throughput, low-latency messaging and can be used for data ingestion and integration in ETL pipelines.
- Amazon S3: S3 is a cloud-based object storage service offered by Amazon Web Services (AWS). It provides scalable and reliable storage for data in ETL pipelines. S3 can be used for data ingestion, staging, and archiving.
- Apache Airflow: Airflow is an open-source platform used for workflow management in ETL pipelines. It provides a way to schedule, monitor, and manage complex workflows across multiple systems. Airflow supports various data sources and destinations and easily integrates with other ETL tools and technologies.
Depending on the specific requirements and use case, data engineers may use a combination of these technologies and tools to build and maintain their data engineering pipelines.
Data engineering is an essential component of modern data management, helping businesses transform raw data into meaningful insights that can aid organizations to make informed decisions and reach their objectives.
Propel provides professional data engineering services that offer solutions to help you fully harness the potential of your data. Connect with us today to discover how we can assist you in gaining a competitive advantage in the data-driven world.