Learn more about data lakes in this FAQ and introduction. Data lakes have evolved significantly in the last decade, as enterprises have gained more real-world experience. If you would like to learn more about SaaS data lakes, please get in touch.
What is a Data lake?
A Data Lake is a complete data environment where a wide variety of data can be ingested, stored, and analyzed for all analytics, including data engineering, data science/AI/ML, and BI. A wide variety of end-users want to access to data lakes, including business/app users, data engineers, data scientists, and BI analysts. The environment should support access to existing analytical tools or allow new analytical tools to be easily accommodated.
[Note: Some vendors will refer to a Data Lake just as a limited data repository (ex. S3/ADLS and/or catalog). Cazena’s definition is broader, and includes analytical and tool capabilities, which is consistent with typical enterprise deployments.]
What is a SaaS Data Lake?
The SaaS Data Lake is a third generation offering that aims to address the skills shortage. SaaS Data Lakes automate provisioning and management, and require zero DevOps effort for deployment and ongoing operations. Learn more about the evolution of data lakes in this blog post and infographic.
Cazena’s SaaS Data Lake as a Service is a turnkey platform that includes data ingestion, storage, compute, PaaS, as well as essential tooling. It includes mechanisms or enables data management tasks such as auditing, logging, data governance. It enables users to be added and removed with appropriate security and policy controls. It also provides a secure environment where data is safe and encrypted both in transit, and at rest.
Why do I need a Data Lake?
Data Lakes can drive faster business outcomes, higher revenue and increased profitability Data Lakes enable easy access and analysis of data as the foundational trigger for this growth. That’s because data lakes can support all data types and analytics. That enables machine learning and predictive use cases like preventive maintenance or proactive customer experience with machine data, marketing analytics, digital product offers, customer segmentation, etc. This is particularly important as the volume and variety of data is growing exponentially – and more companies adopt advanced analytics with R, Python and new ML/AI tools.
Who Uses Data Lakes?
Today’s data users have also changed, expanding from data analysts with SQL/BI tools to modern data scientists and data engineers. Data lakes are also a critical foundation for application developers who want to consume data and analytics with a wide and growing variety of modern analytic tools (ex. R, Python, Tableau, Trifacta, DataRobot, just as a few examples, but there are hundreds of others). Data Lakes offer the most flexible application platform for these tools with built-in support of a wide variety of data processing engines.
What Are Some Real-World Data Lake Case Studies?
Here are examples of three different enterprises leveraging Cazena’s SaaS Data Lake as a Service to drive faster business outcomes.
- CWT for Travel Analytics
- Sentier Analytics for Pharmaceutical Marketing
- Victoria University for Student Experience
Read more case studies on Cazena.com/customers.
How are Data Lakes different from Data Warehouses?
Data Warehouses are well-suited for doing structured analytics (mostly BI or ad hoc SQL) on relational/structured data, typically supporting legacy tooling and reporting (ETL and BI). In contrast, Data lakes seek to create a broader modern data environment with broader set of analytics – data engineering, prep, data science/ML as well as BI processing. Data lakes are ideal to run greenfield workloads in a single unified platform that combines legacy and newer data, supporting various processing engines (SQL, Spark, Hive, NoSQL, etc…) for the full lifecycle of analytics for digital teams.
Done well, Data lakes can enable tremendous flexibility and speed of innovation. Data Lakes can accelerate access to data not just to analysts and data scientists, but also help offload Data Warehouses (as sources of data) or provide downstream data to Data Warehouses for specific business-level reporting or BI needs. Data Lakes and Data Warehouses complement each other and provide a virtuous cycle of data management.