An on-premises data lake stores raw data in its native object storage on infrastructure a company owns and operates, rather than on a cloud provider’s managed storage. It holds structured, semi-structured, and unstructured data side by side, so teams can query it without transforming it first.
In this post you’ll learn how the storage and compute layers in an on-premises data lake work together and how an on-premises deployment compares to a cloud data lake. With modern query engines, you can also analyze on-premises data alongside cloud sources instead of migrating everything first.
Key takeaways
- An on-premises data lake stores raw, multi-format data in its native format on infrastructure your organization owns and runs itself, typically using HDFS or on-premises object storage.
- On-premises data lakes separate into a storage layer and a compute layer. Query engines like Trino can then analyze the data in place without copying it into a separate system first.
- On-premises deployments give you more control over data residency and latency, but they also carry more operational overhead than cloud data lakes; many organizations end up running both.
- Common challenges include managing Hadoop and HDFS infrastructure, siloed access across clusters, and connecting on-premises data to cloud-based analytics and AI tools.
- You can modernize an on-premises data lake with an open table format like Apache Iceberg, or extend it to a hybrid or multi-cloud setup. A third option is to query it in place with a federated engine instead of migrating everything first.
What is an on-premises data lake?
An on-premises data lake is a repository for raw, multi-format data stored on infrastructure your organization runs itself. Examples include on-premises servers, the Hadoop Distributed File System (HDFS), and local object storage. A cloud data lake, by contrast, uses a provider like Amazon Web Services or Microsoft Azure to own and manage the underlying storage.
Meanwhile, the term data lake describes a type of data architecture used to store large amounts of data flexibly and cost-effectively. That storage can live in cloud object storage or on servers you manage on-premises. An on-premises data lake typically holds structured data, such as transaction records, next to semi-structured data like JSON logs and unstructured data such as images or documents. Because the data stays in its native format until you query it, you skip the upfront transformation work a data warehouse needs.
Many organizations built their first data lakes on-premises because that’s where their existing infrastructure and data already lived. Data residency requirements, latency-sensitive workloads, and the sunk cost of existing hardware all push organizations toward keeping some or all of their data on-premises. This holds true even as cloud adoption grows elsewhere in the business.
How on-premises data lakes work
An on-premises data lake separates into two layers, storage and compute. Earlier designs often combined the two, and understanding that change helps explain why on-premises data lakes look the way they do today.
The storage layer
The storage layer holds your data using HDFS or on-premises object storage. HDFS distributes files across a cluster of servers and replicates blocks for fault tolerance. On-premises object storage systems organize data as objects with metadata, similar to how Amazon S3 works, but on hardware you control.
Query and compute layer
The compute layer runs the engines that read and process data sitting in storage. Engines like Trino run directly against the storage layer, separating analytics workloads from the transactional systems that first captured the data. Separating the layers keeps heavy analytical queries from competing with the production systems your business depends on minute to minute.
This two-layer approach differs from earlier designs. On-premises data warehouse systems once combined compute and storage on the same machines, and early Hadoop-based data lakes carried that same pattern forward. Separating the layers gives you more flexibility to scale each independently as your workloads grow.
On-premises vs. cloud data lakes
On-premises data lakes give you direct control over where your data physically sits, which matters when regulations, contracts, or internal policy dictate data residency. You manage the hardware, the network, and the security perimeter yourself, and query latency can be lower for workloads that live close to the data. That control comes with tradeoffs. You’re responsible for capacity planning, hardware refreshes, and scaling compute to meet demand.
Cloud data lakes move that operational burden to the provider. You get elasticity, since you can scale storage and compute independently without buying hardware ahead of need, and you typically pay only for what you use. The tradeoff is less direct control over the physical infrastructure and, for some organizations, added complexity around data egress costs and cross-region latency.
In practice, few organizations run purely on-premises or purely in the cloud. A manufacturing company might keep its primary infrastructure on-premises and in Amazon Web Services. It might then extend an on-premises data lakehouse to the cloud for data residency needs in a specific region. This kind of hybrid setup is common once an organization has meaningful data on both sides.
Common challenges with on-premises data lakes
Running a data lake on-premises means your team owns the operational overhead that comes with Hadoop and HDFS clusters. That includes patching, capacity planning, and hardware replacement cycles, all of which take time away from work that directly serves the business.
Data silos present a second challenge. Teams often provision separate clusters for different projects, and access controls, schemas, and governance policies can drift apart across those clusters. Additionally, connecting on-premises data to cloud-based analytics and AI tools often needs custom pipelines that copy data between environments. These pipelines add latency and create more copies of the same data to secure and govern.
These challenges compound as organizations adopt more cloud services alongside their existing on-premises footprint. A fully centralized setup isn’t always realistic, and the alternative goal is universal data access across on-premises, multi-cloud, and SaaS sources without forcing every workload through one physical location first.
Modernizing an on-premises data lake
Organizations facing these challenges generally choose from a few paths. The first keeps data where it is but adopts an open table format, such as Apache Iceberg, on top of existing on-premises storage. This adds transactional guarantees, schema evolution, and time travel to structured and semi-structured data.
A second path extends the environment to a hybrid or multi-cloud setup. Starburst’s own guidance on migrating from Hadoop to a modern data lakehouse lays out three tracks depending on how much change an organization is ready for:
- SQL-engine upgrade for immediate performance gains without a full migration
- Turnkey on-premises modernization for regulated or latency-sensitive workloads
- Move to a fully managed cloud data lakehouse platform
A third path skips migration entirely and queries on-premises data in place with a federated engine. This approach queries the on-premises data lake as one of several sources. Combined with an open table format, this kind of setup combines the low-cost storage of a data lake with warehouse-style guarantees. The result is a data lakehouse setup without a full migration project.
How Starburst connects on-premises data lakes to the rest of your data
Starburst Enterprise runs on-premises, in the cloud, or across both, which means you don’t have to choose a single deployment model for your query layer. If your data lake stays on-premises for now, Starburst Enterprise runs there too, alongside your existing HDFS or object storage.
With data federation, you can query data where it already lives without migrating it first. Instead of building pipelines to move on-premises data into a cloud data warehouse before you can analyze it, you connect Starburst to your on-premises sources and your cloud sources. You then run a single query across both. Organizations with strict data residency rules benefit most, since data can stay where regulations, latency, or business logic require it, while still remaining accessible through one query layer.
For teams weighing whether to migrate an on-premises data lake or extend it, federation offers a third option. You keep the data where it needs to stay for compliance or performance reasons. You still give analysts and data scientists a single point of access that spans on-premises, multi-cloud, and edge sources. That starting point skips the multi-year migration project entirely. With this approach, you can modernize access to your data before you modernize where that data physically sits.
Next steps
Want to know more about modernizing on-premises data lakes? Check out the Data Lakehouse Buyer’s Guide.
FAQs
What is an on-premises data lake?
An on-premises data lake is a repository for raw, multi-format data that you store on infrastructure your organization runs itself, such as HDFS or on-premises object storage. This differs from a cloud provider’s managed storage. The data lake holds structured, semi-structured, and unstructured data side by side in its native format until you query it.
How is an on-premises data lake different from a cloud data lake?
An on-premises data lake gives you direct control over where your data physically sits, along with the hardware, network, and security perimeter that surrounds it. A cloud data lake moves that operational burden to the provider. You get elasticity and typically pay only for what you use, but you give up some of that direct control over the physical infrastructure.
What technologies power an on-premises data lake?
An on-premises data lake typically runs on HDFS or on-premises object storage for the storage layer, and query engines like Trino for the compute layer. With this separation, you can scale each layer independently while keeping analytical queries from competing with the transactional systems that first captured the data.
What are the biggest challenges with running a data lake on-premises?
The biggest challenges include the operational overhead of patching and scaling Hadoop and HDFS infrastructure and the data silos that form when teams provision separate clusters. Connecting on-premises data to cloud-based analytics and AI tools often needs custom pipelines too. Each of these adds work that takes time away from tasks that directly serve the business.
Can you query an on-premises data lake alongside cloud data without moving it?
Yes. With data federation, you connect a query engine to your on-premises and cloud sources. You then run a single query across both instead of copying data into a cloud data warehouse first. This works well for organizations with strict data residency rules, since data can stay where it needs to while remaining accessible through one query layer.
When should a company modernize an on-premises data lake instead of replacing it?
Modernizing makes sense when your existing infrastructure still works but lacks transactional guarantees or unified access. Options include adopting an open table format like Apache Iceberg, extending to a hybrid or multi-cloud setup, or querying the data in place with a federated engine. Each of these approaches combines the low-cost storage of a data lake with warehouse-style guarantees, improving governance and access without a full migration project.
