Delta Lake with Apache Spark using Scala

Explore Delta Lake with Apache Spark by setting up a Spark cluster, loading data, and applying schema validation, caching, and concurrency controls through hands-on lessons.

Explore Delta Lake, an open source layer for Apache Spark that enables concurrent transactions (insert, update, delete), scalable metadata, and unified streaming and batch processing on existing data.

Describe how a data lake stores data in its natural form as a single enterprise repository for raw and transformed data used in reporting, visualization, analytics, and machine learning.

Explore the four elements of delta lake architecture, including parquet-based data files, the delta log with transaction logs, and object storage, enabling fast, scalable queries via Databricks.

Discover Apache Spark as a high performance, distributed engine that scales across clusters, enabling SQL, structured data processing, streaming, graph processing, and machine learning using Java, Scala, Python, or R.

Visit databricks.com and click get started for free to access the Community Edition. Receive emailed credentials and log in to the Community Edition to practice on Databricks at no cost.

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Provision a spark cluster by logging into the community site, navigating to clusters, naming your cluster, and creating it until the status becomes active.

Discover the basics of notebooks, including creating and naming notebooks, building runnable cells, executing code, and using magic commands for documentation and shell or spark tasks.

Work with dataframes in Spark by loading data with a schema, selecting columns, filtering rows such as price > 2, and using Spark SQL in notebooks to visualize results.

Create and manage delta tables in a Databricks environment by writing sql statements to drop existing tables, define columns and data types, and partition by calendar year for efficient reads.

Learn to write data into a delta table using spark dataframe write with delta format, specifying the file location and partitioning by calendar year with append or overwrite modes.

Read a delta lake table with spark sql in scala from a mounted location. Demonstrate selecting star from delta and handling partitioned data with append and overwrite operations.

Delta Lake validates the data frame schema against the table schema, enforcing column existence, type matching, and case sensitivity to prevent data corruption.

Learn how to update a Delta Lake table schema in Spark by adding a new column, describe the table to verify changes, and save updates through a data stream workflow.

Explore table metadata in delta lake with spark sql by examining a salary table and its columns such as name, location, created, last modified, and how records evolve over time.

Demonstrates deleting 2011 records from a Delta Lake salary table using Spark and Scala, showing counts before and after (about 92k to 85k).

Update a Delta Lake table using Spark and Scala to change calendar year values in a partitioned table from 2000 to 2020, then verify with a 2020 query.

Explore how Delta Lake tracks table history in reverse chronological order using describe history, retrieve the last operation, and explore transaction history for a Delta table.

Explore concurrency control in Delta Lake with Apache Spark using Scala, focusing on maintaining consistent, safe transactions across a data lake.

Understand optimistic concurrency control in Delta Lake, delivering transactional guarantees via read, write, and commit stages, detecting conflicts and raising concurrent modification exceptions to produce a new version snapshot.

Learn how to migrate workloads to Delta Lake, leveraging automatic partition management and transaction log as the source of truth, while avoiding manual refreshes and unsafe external reads.

Explore techniques to optimize performance through file management in Delta Lake with Apache Spark using Scala.

Configure Delta Lake auto optimize for specific data tables and enable optimize write by setting table properties, then apply auto compaction to improve data layout and performance.

Cache data locally to speed up successive reads, optimizing performance with Delta Lake on Apache Spark using Scala.

Explicitly select and cache a subset of data in Delta Lake with Apache Spark using Scala to ensure consistent performance for repeatedly accessed tables.

Explain isolation levels and how they define the degree to which modifications by concurrent transactions are isolated, and note the default level in use.

Apply best practices to improve data lake performance by providing data location hints and selecting a partition column, avoiding high-cardinality fields, and regularly compacting small files into larger ones.

Discover how the optimize command consolidates small files to boost performance. Understand that Delta Lake automatically collects statistics to aid query planning, whether you run optimize or not.

Explore optimization strategies for Delta Lake with Apache Spark using Scala, balancing performance and cost. Learn when to run optimize daily at night and offline.

discover how to select the best compute instance for running the optimizer in Spark workflows, with tips for optimization interview questions and practical guidance on when to use different instances.

Explore auto optimize and concurrent transactions in delta lake with apache spark using scala, addressing interview questions and their impact on streams.

Discover whether to schedule auto optimize for Delta Lake with Apache Spark, see how scheduling consolidates files and handles updates, and learn practical guidelines for when to enable optimization.

Examine the reliability and transaction support of an open-source data lake layer, its compatibility with existing data lakes, and workload-based configuration for optimized indexing and fast interactive queries.

Explore an interview-style FAQ about how data leak relates to a bike spotter. Highlight ideas for data handling within Delta Lake and Apache Spark using Scala.

Answer an interview question about data formats, cloud storage, and transaction tracking in Delta Lake with Spark, including comments, blogs, and e-books as stored data.

Explain how to do things right in an interview question to prevent deadly outcomes, reflecting on the discussed scenarios.

Answer common interview questions about writing data to a specified cloud location within a Delta Lake workflow using Apache Spark and Scala.

Discover Delta Lake with Apache Spark using Scala and work with structured streaming data in this course.

Answer a targeted interview question 13 in a practical faq style, exploring affordability, policy references, and decision-making in real-world product scenarios.

Answer interview FAQ 14 for Delta Lake with Apache Spark using Scala, highlighting table management, automation, and cost-mitigation strategies discussed in the lecture.

Explore which delta lake features are unsupported, and how schema, bucketing, and reading from tables relate to inserts or direct loading in this interview-style FAQ.

Explore how to preserve data types when dropping a column in delta lake with Apache Spark using Scala, framed as interview question 17.

Explore how the U.N. delegation and delegates' rights interact with ballot weight and locking mechanisms, clarifying how conflicts are resolved and how league support may emerge.

Explore limitations and not-supported features in this Databricks environment, including server-side encryption with customer-provided keys, credentials in a cluster, and security token service restrictions.

Celebrate finishing the course and thank students for enrolling. Wish them success in their future endeavors and encourage continued learning.