Understanding Data Pipelines and ETL for Software Engineers

Sandeep Chauhan's photo
Sandeep Chauhan
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7 min read

In software engineering, we’re often focused on building applications, optimizing algorithms, and ensuring robust system architecture. However, as data becomes central to decision-making and machine learning, understanding data pipelines and ETL (Extract, Transform, Load) becomes crucial for any engineer looking to work with large datasets or integrate data-driven features into their applications. Here’s a breakdown of how data pipelines and ETL work, presented in terms that align with your software development background.

What is a Data Pipeline?

A data pipeline is similar to an automated build pipeline in software development. It’s a sequence of steps that moves and processes data from one system to another, transforming it along the way. Just as a CI/CD pipeline handles code from commit to deployment, a data pipeline manages data from its source to its final destination, such as a database, data warehouse, or even a machine learning model.

Key Components of a Data Pipeline:

1. Source: The origin of the data. It could be databases, APIs, flat files (like CSV), logs, or streaming services.

2. Transformation: The intermediate steps that clean, aggregate or reformat the data. This is where business logic is applied to make data usable.

3. Destination: Where the transformed data is sent, which could be storage systems (databases, data lakes) or applications that consume the data for further analysis or real-time decision-making.

In software terms, you can think of this as an end-to-end system where input data flows through several microservices or processing nodes, performing specific tasks at each step.

Understanding ETL (Extract, Transform, Load)

The ETL process is at the heart of many data pipelines. It stands for Extract, Transform, Load, and it’s a common workflow for moving and reshaping data.

1. Extract

The extraction step is focused on retrieving data from various source systems. These sources can be structured (like relational databases), semi-structured (like JSON or XML files), or unstructured (like log files or media). The primary goal of extraction is to access raw data without significantly impacting the performance or availability of the source systems.

Challenges in this step include dealing with data source heterogeneity, ensuring minimal latency for real-time systems, and maintaining data consistency during the extraction process. Advanced techniques such as Change Data Capture (CDC) or event-driven architectures are often employed to handle incremental data efficiently.

Few services used for extraction are:

  • AWS DMS (Database Migration Service): Helps in migrating data from various relational databases into AWS.

  • Amazon Kinesis Data Streams: Used to collect streaming data from sources like IoT devices or log files.

  • Apache Kafka: Kafka acts as a distributed event streaming platform, enabling the ingestion of real-time data from multiple sources.

2. Transform

Once the raw data is extracted, the transformation step comes into play. This step is about cleaning, enriching and structuring the data to meet the requirements of the target system or the analytical use case. Transformations can range from simple tasks like data type conversions and null value handling to complex operations such as aggregations, joins, deduplication, and applying business rules.

This step may also involve deriving new attributes or metrics, normalizing or denormalizing datasets, and validating data quality against predefined standards. The transformation phase is crucial for ensuring that the data is meaningful, reliable, and ready for downstream analysis or processing.

Modern transformation workflows often leverage distributed computing frameworks, such as Apache Spark or AWS Glue for scalability. In more advanced scenarios, transformations may also involve applying machine learning models for predictive analytics or sentiment analysis, using tools like TensorFlow or PyTorch.

Few services used for transformation of data are:

  • Apache Spark: Spark is a distributed computing framework used for data transformation, aggregation, and analytics at scale.

  • AWS Lambda: Serverless computing service that allows you to run code in response to triggers (e.g., transforming data as it flows through streams).

3. Load

The final step, loading, involves writing the transformed data into its target destination. This could be a data warehouse (like BigQuery or Snowflake), a data lake (like Amazon S3 or Azure Data Lake), or an operational database (like PostgreSQL or DynamoDB). The choice of the target depends on the intended use case—data lakes are preferred for storage and exploratory analysis, whereas data warehouses are optimized for structured queries and reporting.

Loading strategies can vary significantly. For real-time systems, streaming data pipelines continuously ingest data into the target system with low latency. In contrast, batch systems aggregate data over a specific time window and load it in discrete chunks. Techniques such as upserts, partitioning, and indexing may also be employed to optimize loading performance and data accessibility.

For instance, a batch process might periodically load transformed sales data into a Snowflake warehouse for BI dashboards, while a real-time pipeline could continuously push processed user clickstream data into Elasticsearch for near-instant search and analytics. Few important services are:

  • Amazon S3: For loading transformed data to a data lake, typically in Parquet or ORC formats for efficient querying.

  • AWS Lake Formation: Helps build and secure data lakes by defining access control and managing data loading workflows.

Batch vs. Real-time Processing

Understanding the difference between batch and real-time (or stream) processing is important in the data engineering world.

Batch processing

This is when data is processed in chunks at a scheduled time (e.g., every hour, daily). It’s efficient for large volumes of data that don’t need to be acted upon immediately, similar to running nightly builds or tests. Example: Aggregating sales data daily to generate reports.

Real-time processing

Here, data is processed as soon as it’s received. This is useful for applications that need up-to-the-second data, like fraud detection or real-time dashboards.

Example: Analyzing user behavior in real-time to make product recommendations.

Database vs Data Warehouse vs Data Lake Vs Data Lake House

  • Database : A database is used to store operational data, typically structured, for day-to-day tasks. It’s optimized for handling real-time transactions and quick reads/writes, like managing customer orders or user profiles. Eg. MySQL, PostgreSQL, MongoDB

  • Data Warehouse: A data warehouse is built for querying and analysis, storing processed and cleaned data that’s typically historical and aggregated. It supports decision-making by enabling complex reporting and analytics, often used for business intelligence. Eg: Amazon Redshift, Google BigQuery, Snowflake

  • Data Lake : A data lake is a large storage system that holds all types of raw data (structured, semi-structured, and unstructured) in its original form, often used for big data analytics, machine learning, or archiving. It’s ideal for storing data before it’s processed or analyzed. Eg: Amazon S3, Azure Data Lake Storage

  • Data Lake House: A data lakehouse combines elements of both data lakes and data warehouses, offering the flexibility of a data lake with the performance and management capabilities of a data warehouse. It stores raw data like a data lake, but also allows for structured, cleaned, and processed data to be queried efficiently like in a data warehouse. The data lakehouse can handle both analytical and operational workloads, making it a versatile choice for modern data architectures. Eg: Google BigLake, or Databricks Lakehouse

Challenges in Data Pipelines

Just like in software engineering, building a data pipeline involves challenges such as:

1. Scalability: Handling large volumes of data efficiently requires scalable storage and processing solutions.

2. Data Quality: Ensuring data accuracy is akin to writing tests for your code. Incorrect data can lead to misleading results, just like bugs in software.

3. Latency: Minimizing the time it takes for data to move from source to destination is crucial in real-time systems, just as performance optimization is key in latency-sensitive applications.

4. Failure Recovery: Like any software system, data pipelines can fail. Engineers use strategies like checkpointing and retries to ensure data is not lost or corrupted.

How LinkedIn’s Data Warehouse Works

At LinkedIn, data warehousing operates a bit differently from traditional systems. Here’s a broader look at how it works:

1. Raw Data Storage: Initially, data is stored as raw Avro files in HDFS or a data lake. This provides a scalable way to store vast amounts of raw data without the overhead of a traditional database.

2. Transformation Process: Using Apache Spark, this raw data is transformed, cleaned, and stored in Avro format in a derived layer.

3. Query Layer: The transformed data is then queried using Presto, which serves as the query engine over this data lake. Presto enables fast, SQL-like queries directly over the file system without requiring the data to be imported into a traditional database.

4. Consumption Layer (Darwin): Finally, Darwin provides an easy-to-use interface for engineers to write SQL queries and analyze the data.

Wrapping Up

In summary, the world of data engineering shares many parallels with software engineering. Data pipelines automate the movement and transformation of data, and ETL is the most common workflow for processing large datasets. Understanding these concepts allows software engineers to design more data-driven applications, collaborate with data teams and take on larger technical challenges in a data-driven world.