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Databricks Certified Associate Developer for Apache Spark 4
Master Apache Spark 4.0 with the complete 2026 hands-on guide for the Databricks Certified Associate Developer exam. Learn PySpark, Spark SQL, Structured Streaming, Spark optimization, and Delta Lake from scratch. Use a verified Udemy coupon code to save up to 93% on this Spark Developer certification course.
by Ansh Lamba JSR · ⭐ 4.6 via Udemy · 6,920 students · English
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$9.99$149.9993% OFF
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✓ 93% discount manually re-verified, not an estimate.
✓ Certificate + lifetime access included by Udemy.
⚠ Redemption-limited — can run out without notice.
Price history First-party data
Tracked since April 19, 2026 to August 10, 2026. Red dot = current price ($9.99) — CoursesWyn records the price on each sync.
Current price
$9.99
Original price
$149.99
Tracked since
April 19, 2026
Last checked
Aug 10
Price and coupon status are recorded from a periodic sync of Udemy’s public catalog. This snapshot was taken on August 10, 2026. Prices are always confirmed at checkout on the provider’s site.
Deal score Computed, methodology below
8.8
/ 10
Discount depth
93%
Udemy rating
4.6 / 5
Popularity
6,920 students
Tracking length
117 days
Score = weighted average of discount depth, how often CoursesWyn re-checks this code, historical price volatility, and how fast past redemptions were claimed. Not an editorial opinion on the course itself.
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Apr 19, 2026Listed by CoursesWyn — price recorded at $9.99Listed
Aug 10, 2026Coupon checked — checkout price $9.99 · listed until Sep 4Working
Course details Synced from Udemy
Fields below are pulled from Udemy’s public catalog and recorded when CoursesWyn syncs — they are catalog data, not editorial description.
| Last checked | August 10, 2026 |
| Level | All Levels |
| Content | 28 hours · 1 downloadable resource |
| Language | English |
| Requirements | Basic Python knowledge will be required. · Basic SQL knowledge will be required. |
| Certificate | Yes, on completion |
| Access | Lifetime, mobile & TV |
What you'll learn From the course description
Understand Spark Architecture and its components - Driver, Executors, Cluster Manager, and how they interact.
Learn about Spark's lazy evaluation model, DAG execution, Catalyst optimizer, Stage and Task execution, and how to monitor Spark applications using the Spark UI
Master PySpark DataFrames and Manipulation techniques including filtering, grouping, exploding arrays, window functions, and advanced aggregations.
Perform joins (inner, left, broadcast, multi-key, anti), unions, and complex transformations such as pivoting, handling nested JSON data, & nested arrays,.
Create Broadcast Variables, Accumulators & User Defined Functions (UDFs & Pandas UDFs).
Execute SQL queries directly on files (CSV, JSON, PARQUET, DELTA & more) and understand all Spark save modes (append, overwrite, and partitioning by column).
Learn how to register DataFrames as temporary views, and work confidently with Date & Time functions including Unix epoch conversions.
Tune Spark performance using partitioning, repartition, coalesce, AQE, shuffle optimization and Salting.
Understand Driver OOM and Executor OOM errors, how executor memory pool works.
Gain insights into Spark Garbage Collection, storage levels like MEMORY_ONLY, DISK_ONLY, caching strategies.
Explore the window operations in streaming data such as tumbling, sliding, and session.
Spark Connect and Deployment modes (local, cluster, client), and how to choose the right one for your application.
Pandas API on Spark and how to use vectorized UDFs for performance optimization.
Delta Lake Optimizations including Z-ordering, data skipping, Liquid Clustering for faster queries.
Build real-time pipelines using Structured Streaming with watermarking & exactly-once guarantees, along with Trigger Types and ForEachBatch operations.
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