A lot of enterprises are choosing Snowflake to become its enterprise data lake. Snowflake’s schema flexibility permits the storage of raw data and on-the-fly schema application, ideal for the often-chaotic nature of data lakes. In terms of performance, Snowflake’s architecture excels in querying and analyzing large datasets, facilitating data lake analytics. Robust security features, encompassing role-based access control, encryption, and auditing, ensure data security and regulatory compliance. Data sharing is also straightforward, providing a secure means for collaboration and potential data monetization. With Snowflake’s continued innovation and flexibility to bring in external tables, iceberg tables, Snowflake is becoming the automatic choice for many enterprises, especially because of its unique architecture of separation of storage and compute.
However with cybersecurity, though Snowflake is an excellent choice for handling security workloads such as Threat detection, ML-powered anomaly detection, and Threat Hunting, the challenges of managing custom data pipelines to centralize log ingestion, the performance of queries, unpredictable costs have often turned away Security teams from realizing the value of migrating from monolithic SIEMs to data lake-powered SIEMs like Snowflake.
DataBahn helps Snowflake users by streamlining data collection and ingestion and removing the onus of your team having to build
customized integrations, pipelines, deploying your staging locations or in the case of the newly released Snowpipe streaming, managing your own Kafka clusters to take advantage of the near-real time data ingestion and availability within Snowflake.
Through DataBahn’s Orchestration capabilities, Security teams can:-
- Simplify data collection and ingestion into Snowflake
- By using DataBahn’s plug-and-play integrations and connectors with a wide array of products and devices.
- By using DataBahn’s native streaming integration for a hassle-free, real time data ingestion into Snowflake.
- By effectively normalizing and structuring data using DataBahn’s orchestration pipelines before the data is loaded into Snowflake tables.
- Convert logs into insights
- By using volume reduction functions like aggregation and suppression to convert noisy logs like network traffic/flow into manageable insights that can be loaded in Snowflake reducing the time for queries to execute.
- Increase overall data governance and data quality
- By identifying and isolating sensitive data sets in transit thereby limiting exposure.
- Perform split-second threat-hunting
- By using the DataBahn’s Indicator Index to extract insights such as Security Observables (IP addresses, Domains, URLs, Hashes), Entity Relationships (Processes, Network execution, Registry modifications), and Intel Context.
- By using additionally derived context such as first observed / last observed time / frequency of observation to speed up data exploration.
- Bring best-of-breed detection technologies
- By leveraging Snowflake marketplace applications with DataBahn forking out data streams to different tables within Snowflake.
- Get visibility into the health of telemetry generation
- By using the dynamic device inventory generated by DataBahn to keep track of devices to identify devices that have gone silent, log outages and detecting any other upstream telemetry blind spots.
- Reduce overall costs of Snowflake
- By removing the need for any staging locations by taking advantage of DataBahn’s native streaming integration to load data directly into tables.
- By routing less frequently accessed data sets using Data Highway to low-cost, cloud storage solutions like S3 while adhering to the same data models and using Snowflake external tables to access them.
- By adopting the use of open data formats like Iceberg for storing data older than your standard retention periods outside Snowflake and using Iceberg tables to access them.
Benefits of using DataBahn with Snowflake
DataBahn offers effortless integration and plug-and-play connectivity with a wide array of products and devices, allowing SOCs to swiftly adapt to new data sources.
DataBahn enriches data against various contexts including Threat Intelligence, User, Asset, and Geo-location, providing a contextualized view of the data for precise threat identification.
The platform supports seamless conversion into any data model of your choosing, additionally facilitating faster downstream onboarding in Snowflake.
Detect changes to log schema intelligently for proactive adaptability and to avoid downstream impact to detections.
DataBahn helps you selectively extract key metadata based on frequency of usage, convert logs into insights to maximize retention of useful data whilst keeping costs of operating the warehouses optimal.
Identify, isolate and mask sensitive data ensuring data security and compliance.
DataBahn’s orchestration platform helps tier data into different storage repos and tables based on its relevance so you can put purpose to your data.
Use the DataBahn AI Ready framework to get your data cleansed, enriched, features extracted, and embeddings generated to build AI powered apps on top of your Snowflake.
Leverage the combined power of DataBahn and Snowflake to deliver data to any external or iceberg tables and use Snowflake to centrally query and access the data.
Use DataBahn to fork out data streams to different tables within Snowflake for restricted data sharing to Snowflake marketplace applications and for internal collaboration.
Discover how Databahn transforms your security incident mitigation and elevates threat-hunting efficiency, and improvement in speed, precision, and effectiveness.
