Today’s data workloads are getting larger. Whereas data architecture of the past might have processed terabytes of data, today that number can easily reach into the petabytes. This trend is only increasing with AI.
For AI to be successful, it requires large amounts of context, which in turn necessitates a robust data pipeline that feeds data in the right way to achieve results.
But there’s another problem.
Not only is your data spread across your organization, but you also need to integrate it to make it useful. This is the domain of data ingestion architecture, and it’s the hidden component in your AI data stack that might be delaying your AI projects.
How do you get the right data to the right place?
At its core, the problem of data ingestion architecture is really two interrelated challenges:
- The need to access the necessary datasets wherever they live – your OLAP databases, data warehouse, APIs, etc.
- The need to integrate that data across systems and make it useful.
You already know that your data lives everywhere. That’s why you’re working to simplify access to data in multiple repositories across the world.
But are you thinking about what you’re going to do with that data once you connect to it?
Why data ingestion is the forgotten hero of the AI revolution
Accessing data is the first and most important step. But you aren’t done there. After access comes ingestion. Ingestion takes the data from various sources (batch and streaming data, APIs, etc.) and makes it usable.
Importantly, ingestion isn’t just one step; it’s a process. This article will unpack that process, explore the importance of data ingestion, and look at how Starburst’s managed data ingestion services can help.
Data ingestion basics
First things first. You can think of data ingestion as the critical first step in data processing, bringing data from its original sources so that it can be made useful.
That’s simple, right? After all, you’re just importing data, right?
In reality, data ingestion at scale is extremely complex, so much so that entire technologies exist to support it.
Further, the data ingestion world is split into two halves. Let’s look at each of these in more detail.
Batch ingestion and file ingestion
Batch ingestion remains a cornerstone of many data architectures, especially for analytics use cases where large datasets need to be refreshed on a scheduled basis. Updates may occur as often as every hour or as infrequently as every few days.
File ingestion is the most common form of batch ingestion. For this kind of process, there is usually a trigger event. For example, file ingestion might be triggered when a new file lands in object storage such as an Amazon S3 bucket, often using an event notification system.
Importantly, batch ingestion pipelines usually handle a variety of data formats. This might include structured data such as CSV files as well as semi-structured data like JSON logs or nested event data. Each format requires different parsing and transformation steps before it can be used for analytics.
Streaming ingestion
Streaming ingestion goes beyond traditional batch-based ETL processes and is designed for real-time use cases such as logistics, manufacturing, or personalization. These scenarios require a continuous flow of data that provides an up-to-the-second view of reality so that automated systems can make accurate decisions.
This type of ingestion is usually powered by specialized stream processing software capable of handling large volumes of data from high-frequency sources such as IoT devices. Within this space, Apache Kafka has become the industry standard. In addition, streaming ingestion often incorporates differential updates delivered directly from source systems using Change Data Capture (CDC), ensuring that changes are reflected quickly and consistently.
How to do data ingestion well
Where do you get started? Building a proper data ingestion process for data processing requires time, testing, and constant monitoring. The three key components are:
Data access and data management
Before you can ingest data, you need to access it. This means that setting up a robust data ingestion framework first requires a strong series of connectors able to access different data sources. This approach not only reduces data silos but also enhances collaboration and data governance.
Ingestion setup and management
Once your data is accessible, you need to begin ingesting it. This is where things can become difficult without the correct data architectural support. Handling this right means configuring systems such as Kafka, which typically consist of multiple scalable components that require specialized expertise to configure and run at scale. If you’re not careful, such systems can quickly become expensive to run.
Operations
Once your ingestion pipelines are complete, they require constant monitoring to ensure that data collection continues to work in a timely fashion and import operations aren’t growing less efficient over time. Ideally, your ingestion system will automatically scale out to handle unexpected spikes, and then scale back in after data volumes decrease to keep costs low.
Benefits of a managed data ingestion service
There are two ways to go about data ingestion.
- A self-managed service
- A fully-managed data ingestion service.
Many companies go the self-managed route. That works alright until the complexity becomes an issue.. Kafka, for example, is known for being hard to learn, developer-unfriendly, and requiring lots of operational investment.
By contrast, a managed data ingestion service does most of the heavy lifting for you. Managed data ingestion leveraging Software as a Service (SaaS) enables you to connect to a wide variety of sources out of the box and customize ingestion to fit your data needs. The service does most of the undifferentiated heavy lifting. That means your teams can spend less time on data ingestion and more time on data value.
Using a managed data ingestion service provides multiple benefits, including:
Breaking down data silos
A managed data ingestion service will provide connectors for the most popular data sources out of the box. This will include support for both batch ingestion via file ingestion connectors to locations such as S3 on AWS, as well as streaming connectors for systems such as Kafka and Confluent (a managed Kafka service).
This enables you to access anything in your data ecosystem, no matter where it lives. It also simplifies performing data integration tasks that bring your most important data into a single location.
Scalability
Adding a new component to your data architecture is typically not an easy process. You have to design for scale, ensuring your workloads are appropriately chunked and the effort is split across properly-sized compute nodes. You also need the ability to monitor your incoming workload and scale out capacity on demand (e.g., a sudden sales spike that floods you with new data). Furthermore, the system requires fault tolerance so that your data processing doesn’t suddenly and unexpectedly grind to a halt.
But this is all undifferentiated heavy lifting. Every ingestion service needs such scalability. That’s why managed data ingestion services support scaling out of the box, taking a large development and maintenance burden off your data engineering team’s shoulders.
Cost optimization
A managed data ingestion architecture can help control costs, both at the point of ingestion and in downstream tasks, such as data integration, data transformation, data validation, and enrichment. It can alert you to sudden spikes in data or unexpected latency in ingestion processing, enabling your data team to resolve issues and right-size the workload quickly.
A managed data ingestion solution can also help you track who’s consuming what data via key metrics. This allows you to identify unused data, which can then be deprecated to save on compute and data storage costs.
Security and governance
Let’s be honest – many homegrown data processing workflows view data security as an afterthought. That spells danger. Ingestion is a centralized entry point for your raw data. That makes it a potential source of sensitive data leaks- either accidental or from internal threats.
With a managed solution, you get security built in. You can federate your directory of choice with the solution and use role-based access control (RBAC) to ensure only those with the proper job roles can view or use a given data stream.
Maintenance
Your work isn’t done after you’ve imported your data into a table in a data lake or data lakehouse. As more data streams in, degradation occurs. Data becomes spread out on disk, snapshots accumulate, and files become orphaned.
Managed data ingestion solutions help here by supporting advanced tools for manual and automated table maintenance. You can schedule maintenance jobs that perform routine tasks such as compacting tables and expiring old snapshots.
Simplifying data ingestion with managed data pipelines
Ultimately, the goal of data ingestion is to get your most critical data into a destination that’s fast, secure, governed, and easy to query, no matter where it lives in the enterprise.
At Starburst, we believe that the best destination for that data is the Icehouse. The Icehouse is an open data lakehouse architecture built on two key components:
- Trino, an open SQL query engine that can query data across your enterprise at petabyte scale, and
- Apache Iceberg is an open table format that offers key improvements over legacy table formats like Hive, including faster performance, schema evolution, and easy table maintenance.
The importance of Managed Iceberg Pipelines
Managed Iceberg Pipelines delivers a fully managed, end-to-end Icehouse experience within Starburst Galaxy, streamlining the entire process of turning raw data into analytics-ready Iceberg tables. From ingestion to optimization, Managed Iceberg Pipelines automates the hardest parts of building and operating a modern data lakehouse.
Managed Iceberg Pipelines support four key features that simplify curating data for fast analytics, AI, machine learning, and data products:
Starburst File Ingestion
Continuously ingest files from cloud object storage into Iceberg tables with ease. No custom ingestion pipelines required, and no infrastructure to manage – just configure and go.
Starburst Streaming Ingestion
Deliver real-time data from Kafka into your lakehouse. Enable teams to react to events as they happen without spending months building a custom real-time streaming infrastructure.
Starburst Live Tables
Transform raw ingested data into trusted, analytic-ready tables using declarative logic built directly into your lakehouse.
Starburst Live Table Maintenance
Keep Iceberg tables lean, fast, and trustworthy with built-in optimization that runs automatically in the background.
Starburst Galaxy makes data ingestion easy
With Starburst Galaxy, you can spend less time on your data ingestion process and transformation infrastructure and spend more time on your data itself. This accelerates time to insight, reduces data engineering overhead, and ensures data remains performant, governed, and AI-ready by default.
