09:32
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34:15
28 Get Started with Delta Lake using Databricks | Benefits and Features of Delta Lake | Time Travel
21:17
27 Read and Write from Azure Cosmos DB using Spark | E2E Cosmos DB setup | NoSQL vs SQL Databases
21:17
17 Read and Write from Azure Cosmos DB using Spark | E2E Cosmos DB setup | NoSQL vs SQL Databases
13:37
01 What is Distributed Computing, Big Data and Hadoop? | History of Distributed File System
10:08
16 Late Data Processing | Watermarks | Tumbling and Sliding Window Operations in Spark Streaming
07:32
15 Tumbling, Sliding and Session Window Operations in Spark Streaming | Grouped Window Aggregations
04:35
14 Spark Streaming Event vs Processing Time | Late Arrival of Data | Stateful Processing |Watermarks
16:53
13 Spark Streaming Handling Errors and Exceptions | Handle Exception for data re-processing in Spark
11:19
12 Spark Streaming Writing data to Multiple Sinks | foreachBatch | Writing data to JDBC(Postgres)
10:31
11 Spark Streaming Triggers - Once, Processing Time & Continuous | Tune Kafka Streaming Performance
10:41
10 Spark Streaming Read from Kafka | Real time streaming from Kafka
14:37
09 Apache Kafka Basics & Architecture | Kafka Tutorial | Pub Sub Architecture | Learn Kafka in 15min
08:44
08 Spark Streaming Checkpoint Directory | Contents of Checkpoint Directory
14:26
07 Spark Streaming Read from Files | Flatten JSON data
03:06
06 Lambda and Kappa Architectures | Data Processing Architectures in Big Data
12:32
05 Spark Streaming Output Modes, Optimization and Background
13:15
04 Spark Streaming Read from Sockets | Convert Batch Code to Streaming Code
07:07
03 Spark Streaming Local Environment Setup - Docker, Jupyter, PySpark and Kafka
06:46
02 How Spark Streaming Works
01:32
01 Spark Streaming with PySpark - Agenda
19:20
26 Spark SQL, Hints, Spark Catalog and Metastore
11:52
25 AQE aka Adaptive Query Execution in Spark
21:17
24 Fix Skewness and Spillage with Salting in Spark
10:30
23 Static vs Dynamic Resource Allocation in Spark
28:17
22 Optimize Joins in Spark & Understand Bucketing for Faster joins
12:35
21 Broadcast Variable and Accumulators in Spark
13:19
20 Data Caching in Spark
15:14
19 Understand and Optimize Shuffle in Spark
16:47
18 Understand DAG, Explain Plans & Spark Shuffle with Tasks
09:42
17 User Defined Function (UDF)
12:37
16 Understand Spark Execution on Cluster
14:08
15 How Spark Writes data
17:50
14 Read, Parse or Flatten JSON data
10:33
13 Read Complex File Formats
17:08
12 Understand Spark UI, Read CSV Files and Read Modes
13:23
11 Data Repartitioning & PySpark Joins
10:28
10 Window Functions, Unique Data & Databricks Community Cloud
10:10
09 Sorting data, Union and Aggregation in Spark
16:15
08 Working with Strings, Dates and Null
12:15
07 Basic Structured Transformation - Part 2
13:06
06 Basic Structured Transformation - Part 1
11:15
05 Understand Spark Session & Create your First DataFrame
03:58
04 Spark DataFrames & Execution Plans
05:45
03 Spark Transformations & Actions
04:47
02 How Spark Works - Driver & Executors
02:55
01 PySpark - Zero to Hero | Introduction
04:46
19 Data Lakehouse | Data Warehousing with PySpark | Analytical Queries on AWS Athena
07:05
18 Data Lakehouse | Data Warehousing with PySpark | Incremental loads with spark-submit
04:44
17 Data Lakehouse | Data Warehousing with PySpark | Sales Fact Load
06:32
16 Data Lakehouse | Data Warehousing with PySpark | Staging JSON Data Sales Fact
05:39
15 Data Lakehouse | Data Warehousing with PySpark | Landing JSON Data Sales Fact
06:03
14 Data Lakehouse | Data Warehousing with PySpark | Product Dimension SCD2 Load
08:03
13 Data Lakehouse | Data Warehousing with PySpark | Customer Dimension SCD2 Load
06:47
12 Data Lakehouse | Data Warehousing with PySpark | Store Dimension SCD1 Load
07:43
11 Data Lakehouse | Data Warehousing with PySpark | Delta Logs, Athena, AWS Glue & Symlink Manifest
06:14
10 Data Lakehouse | Data Warehousing with PySpark | Date Dimension SCD1 Load
05:51
09 Data Lakehouse | Data Warehousing with PySpark | Date Dimension Staging Load
06:12
08 Data Lakehouse | Data Warehousing with PySpark | Date Dimension Landing Load
05:55
07 Data Lakehouse | Data Warehousing with PySpark | Important Utility Functions
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