Companies are moving from traditional data warehouses to data lake solutions because telemetry volumes keep climbing, formats keep multiplying, and budgets are not keeping pace. So what is a data lake, and why should your IT and security teams care? Here's the rundown.
What is a data lake?
A data lake is a centralized repository that stores raw data in its native format, with no predefined structure required. You collect first and apply schema later, only when you actually need it.
That flexibility is the point. A data lake can hold massive volumes of structured, semi-structured, and unstructured data from any source, which lets you explore, correlate, and uncover patterns you did not know to look for. Data lakes are typically built on object storage such as Amazon S3, Azure Blob Storage, or Google Cloud Storage, which keeps the cost per gigabyte low and the ceiling on scale effectively unlimited.
How do data lakes work?
A data lake separates storage from analysis. Data lands cheaply and stays raw, and you decide how to process it when a question comes up. Proper planning still matters, though. Without it, a lake gets disorganized fast. The simplified lifecycle includes ingestion, storage, processing and analytics, and security and governance. First, logs, metrics, traces, and events are collected from applications, infrastructure, cloud services, and endpoints, then loaded into the lake; a good pipeline filters noise, masks sensitive fields, and enriches records before they are written. Next, data sits in its raw format on object storage, with no schema imposed up front, so onboarding new sources does not require a data engineering project. When you need answers, you clean, transform, aggregate, or query the data; you can analyze it in place or move a curated subset into a data warehouse or SIEM for structured analysis. Finally, access controls, encryption, retention policies, and data governance rules protect sensitive information and keep you compliant. This is more important when you are building a security data lake.
Why do you need a data lake?
A data lake removes the tradeoff between keeping data you cannot afford and dropping data you will wish you had. It gives you one affordable place to retain everything, which means broader visibility for investigations, longer retention for compliance, and richer datasets for analytics and AI.
A data lake also reduces silos. When security, IT operations, and analytics teams draw from the same repository, decisions are faster and more consistent. Understanding the difference between a data lake and a data warehouse helps you decide what belongs where. Most teams use both: the lake for retention and exploration, and the warehouse or SIEM for structured, high-value analysis.
What are the benefits of a data lake?
The core benefits of a data lake are lower cost, elastic scale, and the ability to store any data. Each benefit plays out this way:
Cost savings. Traditional data warehouses require a larger upfront investment plus cleansing and transformation before data is useful. A data lake skips that work and stores on commodity object storage.
Scalability. Data lakes grow with you. Whether you add a new cloud account or onboard thousands of endpoints, the storage layer expands without a rearchitecture.
Flexibility. Structured, semi-structured, or unstructured, you store data as it arrives and shape it only when a specific tool or use case needs a specific format.
What are the main data lake challenges?
Data lakes are powerful, but raw data at scale brings recurring problems: quality and governance, security and privacy, and discovery and performance.
Data quality and governance
Raw data is flexible, but it is also messy. Without ownership, cataloging, and retention rules, the lake degrades into a swamp nobody trusts. Managing data lake data well means enforcing governance at ingestion, not after petabytes have piled up.
Security and privacy
Centralizing sensitive information makes a lake a high-value target. You need encryption at rest and in transit, role-based access control, and audit trails to guard against breaches and compliance violations. A well-governed security data lake treats these controls as part of the architecture, not as afterthoughts.
Discovery and performance
Finding the right dataset in a sea of raw files is hard. Weak metadata and missing catalogs leave users guessing. Querying large or complex datasets can be slow when the analytics engine was not built for telemetry-scale volumes. The practical fixes are tiered storage and a search layer that queries data where it lives instead of forcing you to move it first.
From data swamp to single source of truth
Most teams do not struggle with the idea of a data lake. They struggle with the reality of traditional data lake platforms: setting up buckets, wrangling schemas, waiting on cloud tickets, and then discovering the data they finally stored is a pain to get back out. Cribl built its platform around that frustration. As the Data Engine for IT and Security, Cribl provides control and flexibility to make a data lake useful to the people and agents who depend on it.
Cribl Lake is a data lake built for telemetry. It provisions in minutes with minimal configuration, stores data in open formats with a schema-on-need approach, and applies unified retention, security, and access control policies across object stores and clouds. Use Cribl-managed storage or bring your own buckets. Tiered storage aligns cost with data value, and data is not locked into a proprietary format.
Getting data in and out is simple. Cribl Stream collects, reduces, enriches, masks, and routes telemetry from any source into the lake, then replays full-fidelity data back to any SIEM or analytics tool when an investigation or audit requires it. Cribl Edge handles distributed collection at the source. And Cribl Search queries data in place across Cribl Lake, Amazon S3, Azure Blob, Google Cloud Storage, and more, so you find the signal without rehydrating or moving anything.
The result is a data lake that addresses the three challenges above: governed at ingestion, secured by policy, and searchable without delay. Start a free Cribl.Cloud account to try it and see how quickly a working data lake can come together.
Data Lake FAQs
What is the difference between a data lake and a data warehouse?
What kind of data can you store in a data lake?
Any data, including structured database exports, semi-structured JSON and logs, and unstructured files, can be stored in the same repository. For IT and security teams, that usually means logs, metrics, traces, cloud audit trails, endpoint telemetry, and identity events.
A security data lake is a data lake built for security telemetry. It centralizes logs and events from across the enterprise in open formats, applies encryption and access controls, and provides SOC teams with long-term retention that a SIEM alone cannot deliver economically.
Why do data lakes turn into data swamps?
Data swamps happen when raw data piles up without governance, metadata, or a plan for access. If nobody can find or trust the data, it stops being an asset. Cataloging, retention policies, and consistent access controls keep a lake usable.
Do I need data engineers to run a data lake?
Traditionally, yes. Setting up buckets, partitioning, schemas, and access policies took specialized cloud and data skills. Some turnkey options, including Cribl Lake, automate provisioning, retention, and security, so IT and security teams can get started in minutes.
How does a data lake reduce SIEM costs?
You route only high-value, frequently accessed data to your SIEM and store everything else in low-cost object storage. When an investigation or audit needs the rest, you search it in place or replay just the relevant slice back into your tools.
