Streaming ETL - The Fastest Data Integrations | Pros & Cons

CEO, Portable

How Does Streaming ETL Fit Into Your Tech Stack

When does it make sense to use streaming ETL vs. traditional ETL?

What are the benefits of real-time vs. batch processing in data warehousing?

How do you create business value from a streaming ETL process?

This guide will outline the use cases for streaming ETL, the benefits, technical considerations, and the simplest way to get started.

What Is Streaming ETL?

Streaming ETL is the process of syncing data from one system to another in real time. Stream processing typically moves data one record at a time instead unlike batch processing where information is grouped in a queue before it is moved.

Which Vendors Offer Streaming ETL Solutions?

Here are the streaming ETL tools you should evaluate:

  • Kafka (Open Source)

  • Confluent

  • Striim

  • AWS Glue

  • Google Cloud Dataflow

  • IBM Infosphere

  • HVR (Fivetran)

  • Amazon Kinesis

  • Oracle Golden Gate

  • Popsink

  • StreamSets

  • Skippr

  • Debezium (Open Source)

  • Estuary

  • Meroxa

  • Decodable

  • Materialize

  • Talend

As you dig into the ecosystem, we would recommend familiarizing yourself with a few key concepts - Apache Kafka, Apache Spark, file formats (Avro, Parquet, CSV), schemas and data types, and immutable data sets.

Do Data Teams Need Real-Time Data Analytics?

Most data teams do not need real-time data analytics for common business intelligence or process automation use cases. They simply need processing of data that is near real-time and can be processed in batch(measured in minutes instead of milliseconds).

Just like any other component of your data stack, it's always important to consider: 1) the value you can create for business users along with 2) the scalability of the technical data platform you are creating.

You need to ask yourself:

  • How does a streaming platform enhance my company's end-to-end data analytics?

  • Am I able to automate previously manual workloads?

  • Can I sell a new data product to clients if I leverage a real-time pipeline?

  • Can I mitigate risks for my business?

Stream processing is powerful, but you need to make sure it creates value for your specific business.

What Are the Benefits of Streaming ETL Pipelines?

Streaming ETL pipelines provide extremely low latency and high throughput data processing. The benefits include:

1. Different systems remain in sync

2. Data is processed only once

3. Data can be aggregated while in motion

When you have data from different sources (applications, data providers, partners) that need to be processed, a streaming pipeline can offer a strong backbone for real-time analytics, data visualization, or machine learning at scale.

What Are the Differences Between Batch and Streaming?

The differences between batch ETL and streaming ETL: Batch processing involves syncing data on a cadence whereas a streaming data pipeline incrementally applies data transformations as new information arrives.

What Are the Use Cases for Streaming ETL?

Streaming data pipelines are valuable for real-time use cases such as:

  1. High-frequency trading

  2. Real-time user journey personalization

  3. Preventing credit card fraud

  4. Internet of Things (IoT)

  5. Optimizing eCommerce inventory

  6. Improving supply chain bottlenecks

  7. Up-to-the-minute freight tracking

Most teams either 1) engage a data consultant to build their streaming data pipelines, or 2) hire a senior developer in-house to manage the technology.

What Is Near Real-Time Data Processing?

In most scenarios, analytics teams do not need real-time pipelines (millisecond latency). Instead, they need data replication that takes place in near real-time (minutes latency).

Stream processing is phenomenal when you need to ingest, aggregate, and sync data very quickly. For most business use cases, you just need a no-code connector that pulls from a SaaS application and loads the processed data into your warehouse or data lake for analytics.

In these scenarios, it's rarely worth building a data integration from scratch. If you do, you'll need to:

  • Read API documentation

  • Deploy infrastructure that can scale to big data sets

  • Write algorithms

  • Version control your logic (GitHub or elsewhere)

  • Make API requests

  • Process API responses (JSON, XML, etc.)

  • Validate data to detect data loss or anomalies

  • Load the transformed data into your destination

  • Maintain everything

You're probably thinking. Isn't there a better way?

How Do You Get Started With Portable for Near Real-Time ELT? (In Under 5 Minutes)

Here's how you get started with a near real-time ELT pipeline using Portable.

  1. Create an account (with no credit card necessary)

  2. Connect a data source

  3. Authenticate with your data source

  4. Select a data warehouse and configure your credentials

  5. Connect your data source to your analytics environment

  6. Run the flow to start replicating data from your source to your warehouse

  7. Use the dropdown menu to set your data flow to run on a cadence

Next Steps

Want to focus on data analysis instead of infrastructure?

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