🎉 Welcome to the 4th part of Delta Lake essential fundamentals: the practical scenarios! 🎉 There are many great features that you can leverage in delta lake, from the ACID transaction, Schema Enforcement, Time Traveling, Exactly One semantic, and more. Let’s discuss two common data pipelines patterns and solutions: Spark Structured Streaming ETL with DeltaLake that serves multiple Users Spark Structured Streaming- Apache Spark structured steaming are essentially unbounded tables of information.
Let’s understand what are Delta Lake compact and checkpoint and why they are important. Checkpoint There are two known checkpoints mechanism in Apache Spark that can confuse us with DeltaLake checkpoint, so let’s understand them and how they differ from each other: Spark RDD Checkpoint Checkpoint in Spark RDD is a mechanism to persist current RDD to a file in a dedicated checkpoint directory while all references to its parent RDDs are removed.
In the previous part, you learned what ACID transactions are. In this part, you will understand how Delta Transaction Log, named DeltaLog, is achieving ACID. Transaction Log A transaction log is a history of actions executed by a (TaDa 💡) database management system with the goal to guarantee ACID properties over a crash. DeltaLake transaction log - DetlaLog DeltaLog is a transaction log directory that holds an ordered record of every transaction committed on a Delta Lake table since it was created.
🎉 Welcome to the first part of Delta Lake essential fundamentals! 🎉 What is Delta Lake ? Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads. DeltaLake open source consists of 3 projects: detla - Delta Lake core, written in Scala. delta-rs - Rust library for binding with Python and Ruby. connectors - Connectors to popular big data engines outside Spark, written mostly in Scala.