Data Lake
Exabeam Data Lake is a security log management and analytics platform built on top of ElasticSearch, offering a horizontally scalable architecture for ingesting, storing, and searching machine data.
Publisher review
Exabeam Data Lake is a security log management and analytics platform built on top of ElasticSearch, offering a horizontally scalable architecture for ingesting, storing, and searching machine data. It is designed for security operations teams and SOC analysts who need to collect and analyze logs from local, remote, or cloud machines without the cost unpredictability of volume-based pricing. The platform supports both server-side and agent connectors for data collection, and it includes a security information model with highlighted fields to accelerate investigations. Exabeam Data Lake is part of the broader Exabeam Fusion SIEM ecosystem, which also includes UEBA and automated investigation capabilities, making it suitable for organizations looking to reduce manual tuning and improve threat detection workflows.
The platform leverages thousands of out-of-the-box parsers to normalize data from diverse sources, and it provides pre-built reports, dashboards, and visualizations for common security use cases. Its search functionality includes a timeline view and events table, enabling analysts to navigate log data efficiently. The underlying ElasticSearch foundation allows for unlimited scalability, meaning organizations can ingest growing data volumes without re-architecting the system. Exabeam Data Lake also integrates with the company's behavioral analytics engine, which uses machine learning to establish baseline user and entity behavior and detect anomalies such as credential misuse or lateral movement, with dynamic risk scoring aligned to the MITRE ATT&CK framework.
Exabeam competes directly with Splunk and Trend Micro in the SIEM and log management market. Compared to Splunk, Exabeam emphasizes a flat, user-based pricing model instead of Splunk's workload-based pricing, which can lead to unpredictable costs at scale. Exabeam was named a Leader in the 2021 Gartner Magic Quadrant for SIEM for the third consecutive time and received a 4.5 out of 5 rating in the 2021 Gartner Peer Insights Voice of the Customer for SIEM, with 89% of reviewers recommending the product. However, some users note that Splunk offers more customizability, while Exabeam is more focused on UEBA and automated detection.
Despite its strengths, Exabeam Data Lake has notable trade-offs. Some users reported disappointment with the migration path to a new platform, describing it as not yet robust and lacking some functionalities of the previous version. Expectations regarding behavioral analytics functions have not always been met, with mixed results reported by reviewers. Additionally, the platform is less customizable than Splunk for organizations that require deep, ad-hoc querying or extensive custom dashboards. The reliance on ElasticSearch means that while scalability is strong, performance tuning may still require expertise for very high-volume environments.
How it works
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Unlimited scalability
Built on ElasticSearch, the platform scales horizontally to ingest and store growing log volumes without re-architecting.
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Flat, user-based pricing
Charges per user rather than per GB of data, eliminating cost surprises from volume spikes.
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Thousands of out-of-the-box parsers
Pre-built parsers normalize data from hundreds of log sources, reducing manual parsing effort.
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Pre-built reports and dashboards
Includes ready-to-use visualizations for common security metrics, speeding up time to insight.
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Security information model with highlighted fields
Standardizes key security fields across data sources, making searches and correlations more consistent.
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Search with timeline view and events table
Provides a chronological timeline and tabular event view for investigating log data.
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Server-side and agent connectors
Supports both agentless (server-side) and agent-based collection from local, remote, or cloud machines.
Strengths and trade-offs
Strengths
- Unlimited scalability via ElasticSearch allows organizations to ingest petabytes of log data without capacity planning bottlenecks.
- Flat, user-based pricing eliminates the cost unpredictability common with volume-based models like Splunk's.
- Thousands of out-of-the-box parsers reduce onboarding time for new log sources from days to hours.
- Pre-built reports and dashboards aligned to the MITRE ATT&CK framework accelerate detection coverage assessments.
Trade-offs
- Some users found the migration path to a new platform disappointing, describing it as not yet robust and missing existing functionalities.
- Mixed expectations regarding behavioral analytics functions, with some reviewers reporting results that did not meet their needs.
- Less customizability than Splunk for organizations requiring deep ad-hoc querying or extensive custom dashboard creation.
- Performance tuning for very high-volume environments may still require specialized ElasticSearch expertise.
Pricing context
Flat, user-based subscription pricing; no volume-based charges per GB of data ingested. Exact per-user figures are not publicly disclosed and vary by deployment (cloud vs. on-premises) and included modules.
Getting started with Data Lake
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Sign up for Exabeam Data Lake
Visit the Exabeam website and request a demo or trial. Provide your organization details and specify your deployment preference (cloud or on-premises). After approval, you will receive login credentials and setup instructions.
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Connect your log sources
Install the Exabeam agent on your machines or configure server-side connectors for remote and cloud sources. Use the provided documentation to set up data collection from your security devices, servers, and applications.
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Configure parsers and normalization
Select from thousands of out-of-the-box parsers to normalize your log data. Map your log sources to the appropriate parsers in the configuration interface to ensure consistent field extraction and security information model alignment.
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Run your first search
Open the search interface and enter a query for a common security event, such as failed logins. Use the timeline view and events table to review results. Apply filters to narrow down the data and verify that logs are ingesting correctly.
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Set up a pre-built dashboard
Navigate to the dashboards section and choose a pre-built report aligned with the MITRE ATT&CK framework. Customize the time range and data sources. Schedule the dashboard to refresh automatically for ongoing monitoring.
Frequently Asked Questions
What is Exabeam Data Lake and how does it work?
Exabeam Data Lake is a security log management and analytics platform built on ElasticSearch. It ingests, stores, and searches machine data from local, remote, or cloud machines using server-side and agent connectors. It normalizes data with thousands of out-of-the-box parsers for faster investigations.
How does Exabeam Data Lake pricing differ from Splunk?
Exabeam uses flat, user-based subscription pricing instead of Splunk's volume-based model. This means organizations pay per user, not per gigabyte of data ingested, eliminating cost unpredictability from data spikes. Exact per-user figures vary by deployment and modules but are not publicly disclosed.
What are the key features of Exabeam Data Lake?
Key features include unlimited horizontal scalability via ElasticSearch, thousands of out-of-the-box parsers for data normalization, pre-built reports and dashboards aligned to MITRE ATT&CK, a timeline view for investigations, and both server-side and agent connectors for flexible data collection from various sources.
How does Exabeam Data Lake compare to Splunk for SIEM?
Exabeam competes directly with Splunk, emphasizing flat user-based pricing versus Splunk's workload-based costs. Exabeam is more focused on UEBA and automated detection, while Splunk offers greater customizability for ad-hoc querying and dashboards. Exabeam was named a Leader in the 2021 Gartner Magic Quadrant for SIEM.
What are the weaknesses of Exabeam Data Lake?
Some users report a disappointing migration path lacking robust functionality and missing features from previous versions. Behavioral analytics results have been mixed, and the platform is less customizable than Splunk for deep ad-hoc queries. High-volume environments may require specialized ElasticSearch expertise for performance tuning.
Is Exabeam Data Lake scalable for large log volumes?
Yes, Exabeam Data Lake is built on ElasticSearch, allowing horizontal scaling to ingest and store petabytes of log data without re-architecting. This unlimited scalability helps organizations handle growing data volumes, though performance tuning for very high-volume environments may still require specialized expertise.
Alternatives
How Data Lake compares
Direct head-to-head against 3 competitors. Picked by 7wData.
Data Lake
- Pricing
- Flat, user-based subscription pricing; no volume-based charges per GB of data ingested. Exact per-user figures are not publicly disclosed and vary by deployment (cloud vs. on-premises) and included modules.
- Target
- Exabeam Data Lake is a security log management and analytics platform built on top of ElasticSearch, offering a horizontally scalable architecture for ingesting, storing, and
- Strength
- Unlimited scalability via ElasticSearch allows organizations to ingest petabytes of log data without capacity planning bottlenecks.
- Watch for
- Some users found the migration path to a new platform disappointing, describing it as not yet robust and missing existing functionalities.
Amazon S3
- Pricing
- Pay-as-you-go per GB stored; $0.023/GB for first 50 TB/month
- Target
- Teams needing scalable, low-cost object storage for raw data
- Deployment
- Cloud only
- Strength
- Massive scalability and durability with 99.999999999%
- Watch for
- Complex cost management; egress fees can escalate
Databricks Lakehouse
- Pricing
- Custom/Contact sales; DBUs per workload
- Target
- Organizations wanting unified analytics and AI on data lakes
- Deployment
- Cloud only
- Strength
- Delta Lake open format for ACID transactions on data lakes
- Watch for
- Pricing can escalate with compute usage; vendor lock-in risk
Azure Data Lake
- Pricing
- Pay-as-you-go; $0.0208/GB/month for hot tier
- Target
- Enterprises already in Azure ecosystem needing managed lake storage
- Deployment
- Cloud only
- Strength
- Deep integration with Azure Synapse and Power BI
- Watch for
- Complex pricing with multiple tiers and egress costs
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Sources
Reporting on this tool draws on these publicly available sources.