![]() Lakehouse- enable ‘Data Warehousing’ using a ‘Data Lake’ (1:31:15)ĭelta Lakehouse- enable ‘Data Warehousing’ using a ‘Data Lake’ with Delta Capabilities, providing transactional guarantees using transaction logs (ACID-based logs). Update: Latency is now low enough to do this. Data warehousing entirely in a data lake depends on lowering latency enough to allow the gold layer to run from the data lake directly. Gold Layer in Data Lakehouse or Data Warehouse (1:36:25) ![]() Having compute and storage on the same machine gives you very fast access to the data (traditional data warehouse), but these performance benefits can be achieved to a large extent by caching to ‘local disk’ aka DB I/O cache on Databricks. ![]() There might be marginal differences in performance between Databricks SQL and Snowflake depending on the use case. With the more recent introduction of Databricks SQL, we now have similar analytics capabilities as Snowflake, without tying down the data format in Databricks. The key benefit of Snowflake over Databricks was historically the ability to query massively in parallel without performance degradation. The Databricks Lakehouse combines the ACID transactions and data governance of enterprise data warehouses with the flexibility and cost-efficiency of data lakes to enable business intelligence (BI) and machine learning (ML) on all data. Similarly for Databricks, storage and compute are decoupled, but data is not stored in a proprietary format. Snowflake decouples storage and compute but the downside to Snowflake is that data is stored/consumed on a Snowflake proprietary format. This will enable you to gain centralized access to the operational metrics for your data in near real-time. Delta Lake is an open-source storage layer within the Lakehouse which runs on an existing Data Lake and is compatible with Synapse Analytics, Databricks, Snowflake, Data Factory, Apache Spark APIs and guarantees data atomicity, consistency, isolation, and durability within your lake. Cloudera and even Databricks, which recently announced support alongside its own delta lake format. DeltaOMS provides a solution for automatically collecting Delta Transaction logs and associated operational metrics/statistics from Delta Lakehouse tables into a separate centralized database. Lakehouse is underpinned by widely adopted open source projects Apache Spark, Delta Lake and MLflow, and is globally supported by the Databricks Partner Network. For most ‘Data Warehouse Products’, machines are typically up and running whether or not it’s being used because storage and compute are typically coupled. That same in-memory acceleration is critical to enabling the lakehouse functionality. With Databricks, your data is always under your control, free from proprietary formats and closed ecosystems. ![]()
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