Streaming data is a never-ending flow of information that powers modern businesses. A constant stream of updates, clicks, purchases, and interactions – all valuable nuggets waiting to be processed. But raw data is not always useful. These real-time data feeds require a streaming ETL pipeline to refine and structure them into valuable insights.
Now, making this happen, setting up streaming architecture to deal with different data formats, guaranteeing low latency, and ensuring high throughput – that is where the real challenge lies.
This step-by-step guide will teach you how to overcome this hurdle. In 8 simple steps, you will learn to design and deploy streaming pipelines for real-time analytics and immediate value. We will also discuss useful strategies and best practices you should follow to get the most out of your streaming ETL pipelines.
What Is An ETL Pipeline?
An ETL (extract, transform, load) pipeline refers to the process of extracting data from multiple sources, transforming the data into a structured format, and loading it into a target data store for analysis.
ETL plays a major role in data warehousing and business intelligence. It centralizes data into a single repository and makes it accessible for various analytical and business intelligence activities. Data analysts, scientists, and business analysts can then use this data for tasks like data visualization, statistical analysis, model building, and forecasting.
ETL process involves the following 3 steps:
The first step is to Extract data from diverse sources like transaction databases, APIs, or other business systems.
Next, this raw data is Transformed into a structured format. This step typically includes cleaning, summarizing, and restructuring the data to make it suitable for specific business needs and analyses.
Finally, the transformed data is loaded into a data store, like a cloud-hosted database or data warehouse, for deeper analytics and business intelligence purposes.
Let us now look at how an ETL pipeline is different from a simple pipeline:
First, an ETL pipeline can gather data from various sources, each with its unique data formats and structures. A simple pipeline, on the other hand, may just move data from one system to another.
Second, the ETL pipeline can also transform data. This involves cleaning, validating, filtering, enriching, and reformatting it to align with business requirements. This critical step goes beyond mere data transfer and is not present in basic data movement processes.
Finally, ETL integrates data from various systems into a single, unified view that is purpose-built for business reporting and analytics. Simple streaming data pipelines do not necessarily create this integrated data warehouse or BI dataset.
2 Types Of ETL Pipelines
There are 2 main types of ETL pipelines: Batch ETL Pipeline and Streaming ETL Pipeline. Each serves unique purposes and is suitable for different scenarios. Let’s discuss them in detail:
I. Batch ETL Pipeline
A batch ETL pipeline collects data from sources on a scheduled basis, like once a day or week. It then processes the batched data through the extract, transform, and load steps.
Specifically, the batch ETL:
Extracts data from sources like databases or files on a timed schedule in larger chunks.
Stores the extracted data temporarily until there is enough to process.
Transforms the accumulated data as a batch to prepare it for analysis and reporting.
Loads the refined batched data into a target database or data warehouse.
The batch processing approach works well for high data volumes that do not need continuous processing. It provides flexibility on when to run the ETL process. The downside is the latency between data arrival and availability for use.
Because of this, they are ideal for situations where a delay in data processing is acceptable. Examples include monthly sales reports, historical data analysis, and data consolidation for strategic planning.
II. Streaming ETL Pipeline
A real-time streaming ETL pipeline collects and processes data in real-time as it arrives from the source data systems. It offers near-instant data availability for analytics and operations.
In particular, the streaming ETL process:
Continuously extracts incoming data from sources like application logs or sensors.
Immediately processes each data record through the transform stage as it arrives.
Directly loads the processed data into the target database without batching.
Streaming ETL is ideal for sources that have high frequencies and lower volumes. It minimizes delays for urgent data requirements. However, it demands more infrastructure resources to manage the needs of real-time processing.
This type of ETL is essential in scenarios where immediate data processing is crucial. Examples include:
Gaming analytics
IoT data processing
Clickstream analysis
Real-time fraud detection
Social media sentiment analysis
Setting Up A Streaming ETL Pipeline: 8-Step Process
When setting up a streaming ETL pipeline, you need to carefully plan and execute the whole process. Let’s now discuss 8 major steps that will help you build a robust system capable of handling real-time data processing and analysis.
Step 1: Define Your Data Sources
The first step in setting up a streaming ETL pipeline is to identify your data sources. Common real-time data sources include:
Internet of Things (IoT) devices that produce vast amounts of real-time data.
System logs, application logs, and security logs that provide operational intelligence.
Web and mobile applications, that can provide user interaction data. These systems often generate a continuous flow of user activity logs.
Document the data format (JSON, XML, CSV etc.) and communication protocols (HTTPS, MQTT, etc.) each source uses. This is necessary for setting up the right data ingestion adapters later.
Step 2: Choose A Streaming Platform
When setting up a streaming ETL pipeline, one of the most important decisions is choosing the right streaming platform. There are several leading options to consider:
Amazon Kinesis is a fully managed streaming service offered as part of AWS. A major benefit of Kinesis is its tight integration with other AWS services like S3, Redshift, and Lambda.
Google Pub/Sub is a global messaging service that lets you transmit data between applications and services. Pub/Sub offers automatic scaling, low latency, and configurable message retention periods.
Apache Kafka is a popular open-source platform that provides high throughput and scalability. It has a distributed architecture that allows large volumes of real-time data to be ingested and processed efficiently.
Each of these platforms has unique strengths:
Kinesis: Best for AWS-centric workflows, offering smooth integration with AWS services.
Google Pub/Sub: A go-to for projects within GCP, ensuring easy integration and global scalability.
Kafka: Ideal for high-volume data handling with a focus on real-time processing.
Step 3: Set Up Data Collection
Now you need to establish a robust data collection method. This process is crucial because it forms the foundation where the entire ETL pipeline operates. There are a few methods we can use to ingest streaming data:
Log file capturing: You can configure your data sources – whether databases, servers, applications, etc. – to write activity logs. These log files capture events in real-time and can be tailed by our ETL pipeline for constant data streams.
API streaming: Many modern data sources provide real-time data APIs. We can connect to these APIs in a streaming manner to get continuous feeds of the newest data. This method scales better than log tailing.
Device data integration: For IoT solutions, sensors, edge devices, or microservices are directly connected to the pipelines to collect data. This offers low latency streams but requires careful connection management.
One way to smoothly and efficiently collect data from different data sources is to use a solution like Timeplus. Timeplus offers easy connections to a variety of sources like Apache Kafka, Confluent Cloud, and Redpanda, as well as file uploads. Plus, it supports data ingestion via REST APIs and SDKs across multiple programming languages, catering to diverse developer needs.
Step 4: Design The Transformation Logic
When working with streaming data, you should transform and enrich it on the fly before loading it into the target data warehouse or data lake. Here are some points to consider when designing the data transformation logic:
Filtering irrelevant data: This involves removing unnecessary or redundant data from the stream.
Data enrichment: This transformation adds additional context or information to the data stream.
Data normalization: This process transforms data into a consistent format to ensure compatibility and ease of analysis.
Step 5: Implement Data Transformation
Once you have designed your transformation logic, you can implement it using a stream processing platform like Apache Flink, Google Cloud Dataflow, and Apache Spark Streaming. Streaming SQL is also a great language for expressing data transformations in real-time.
When implementing transformations with streaming engines:
Manage system state to accurately track results over time as data arrives.
Set event time watermarks to define windows for time-based processing.
Use time windows, like tumbling and sliding windows to aggregate data.
Perform calculations and enrichments based on event time and ordering.
Timeplus is equipped with a high-performance streaming SQL engine and excels in processing streaming data efficiently. The engine uses modern parallel processing technologies, like SIMD (Single Instruction/Multiple Data), to provide high-speed data processing.
Step 6: Choose A Target Data Store
When setting up a streaming ETL pipeline, an important factor is choosing an appropriate target data store to load the transformed data into. The target data store should match the structure and needs of your data. A few options are:
Data warehouses: When scalability and ease of use are needed, cloud-based data warehouses like Snowflake are an attractive option. Data warehouses combine the power and query capabilities of a warehouse with the flexibility of the cloud.
NoSQL databases: For unstructured or semi-structured data like logs, sensor readings, or social media feeds, NoSQL databases are more fitting. Their scalability, flexibility, high write throughput, and dynamic schemas allow easy ingestion of heterogeneous rapidly evolving data.
SQL databases: For data that conforms to a strict schema, SQL databases are the preferred option. Relational SQL databases organize data into tables with predefined columns and data types. This works well for structured log or transaction data that needs to be queried via SQL.
Timeplus, while not a data store in itself, plays a crucial role in this ecosystem. Its connectors and integration features provide seamless transfer of transformed data to various databases, as well as data lakes like S3.
This flexibility allows you to channel streaming data into your chosen target store efficiently and reliably, ensuring a smooth and scalable data-handling process.
Step 7: Load Data Into The Target Store
When loading data into the target data store, you have 2 main strategies:
Micro-batching: This involves grouping small, manageable sets of data and loading them periodically. This strategy is effective for balancing the immediacy of data availability with resource optimization. It suits scenarios where near real-time data is sufficient.
Continuous updates: Alternatively, you could opt for continuous updates where data is loaded as it arrives. This approach is ideal for real-time data processing, where immediate data availability is important. It does, however, require a more robust infrastructure to handle the constant data flow.
A key aspect of the loading process is guaranteeing data consistency and integrity. This involves:
Validating data to make sure that it meets quality standards.
Ensuring that the loading process doesn't lead to data loss or duplication.
Implementing error handling mechanisms to manage any discrepancies or issues in the data.
Step 8: Monitor & Optimize the Pipeline
Closely oversee your streaming ETL pipeline once it becomes operational. This helps you to identify and address any bottlenecks or issues that impact performance. This provides visibility into key parameters like:
Latency in transformations
Errors/retries during processing
Data ingestion rates from sources
Data delivery rates into target stores
Resource utilization across the pipeline
In addition to monitoring, you need to optimize your ETL pipeline periodically. As data volumes or complexity of transformations increases, new bottlenecks can arise over time. Make sure to conduct regular pipeline audits to check for the following:
Potential to scale out pipeline stages as needed
Spikes in reading/writing latency from data stores
Ways to improve data quality checks in processing
Changing needs for data routing, filtering, or aggregation
Compute or memory bottlenecks in transformation code
You can use Timeplus to monitor your streaming data pipeline. Its ability to process streaming and historical data quickly using SQL provides an intuitive way to observe the ETL process's performance and health.
5 Best Practices For Running Streaming ETL Pipelines
When operating streaming ETL pipelines, take a strategic approach toward data quality, scalability, error handling, optimization, and maintenance. Let’s discuss them in detail.
A. Ensure Data Quality At Every Stage Of The ETL Process
To maintain high data quality throughout your streaming ETL process, adopt a multi-layered approach:
Implement mechanisms to correct data irregularities.
Continuously monitor data for anomalies or unexpected patterns.
Regularly check data accuracy and completeness at every stage.
Ensure consistent data structures and enforce data type restrictions.
Keep metadata updated for easier traceability and understanding of data lineage.
B. Scale Your Infrastructure To Match Data Throughput & Processing Needs
The streaming architecture should seamlessly scale up or down to match real-time data volumes and throughput. For this, you should:
Distribute workload evenly across servers to maximize efficiency.
Implement systems that automatically scale up or down based on current processing needs.
Use cloud services or virtualization to dynamically allocate resources based on demand.
C. Implement Robust Error Handling & Recovery Mechanisms
Robust error handling is essential for streaming ETL:
Set up real-time alerts for any system anomalies or failures.
Implement comprehensive logging to record errors and irregularities.
Make sure that your system can switch to a backup operation mode in case of a failure.
Use a dead-letter queue for failed message processing, enabling later analysis and reprocessing.
D. Optimize Data Transformations For Efficiency & Performance
Continuously tune ETL performance through partitioning, caching, batching, parallelization, etc. To enhance performance:
Regularly review and optimize query performance.
Simplify data transformation logic to reduce processing time.
Use in-memory data stores for faster data access and processing.
Break down tasks into smaller units that can be processed in parallel.
E. Regularly Update & Maintain Your ETL Pipelines
Conduct regular audits to identify any potential issues or areas for improvement. To keep your ETL pipelines running effectively:
Monitor for changes to source data formats, APIs, and connectivity.
Keep your team trained and ensure that documentation is up to date.
Document and track all changes to the ETL process to avoid disruptions.
Implement Continuous Integration/Continuous Deployment (CI/CD) practices for smooth and frequent updates.
Conclusion
A well-constructed streaming ETL pipeline can make a big difference in various industries. It helps improve customer experience by giving them personalized services and also makes things run smoother behind the scenes, boosting efficiency and sparking new ideas.
However, building custom streaming architecture from scratch can be complex. This is where Timeplus can help – offering an intuitive, no-code platform to develop streaming ETL pipelines with ease. With Timeplus, you get out-of-the-box connectors to data sources, built-in streaming SQL engine for transformations, and integration with data targets like databases and data lakes.
So if you want to unlock the potential of real-time data without engineering headaches, choose Timeplus. Sign up now for a trial account to experience Timeplus for yourself or book a live demo.