Spark ETL with Lakehouse | Apache Iceberg

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In this video, we are discussing Spark ETL with Lakehouse | Apache Iceberg

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We will discuss Spark ETL pipelines with all of the different types of sources. Detailed Plan:

0. Chapter0 - Spark ETL with Files (JSON | Parquet | CSV | ORC | AVRO)
1. Chapter1 - Spark ETL with SQL Database (MySQL | PostgreSQL)
2. Chapter2 - Spark ETL with NoSQL Database (MongoDB)
3. Chapter3 - Spark ETL with Azure (Blob | ADLS)
4. Chapter4 - Spark ETL with AWS (S3 bucket)
5. Chapter5 - Spark ETL with Hive tables
6. Chapter6 - Spark ETL with APIs
7. Chapter7 - Spark ETL with Lakehouse (Delta)
8. Chapter8 - Spark ETL with Lakehouse (HUDI)
9. Chapter9 - Spark ETL with Lakehouse (Apache Iceberg)
10. Chapter10 - Spark ETL with Lakehouse (Delta vs Iceberg vs HUDI)
11. Chapter11 - Spark ETL with Lakehouse (Delta table Optimization)
12. Chapter12 - Spark ETL with Lakehouse (Apache Kafka)
13. Chapter13 - Spark ETL with GCP (Big Query)
14. Chapter 14 - Spark ETL with Hadoop (Apache Sqoop)
  1. Developer's Home
  2. Spark
  3. Databricks
  4. Spark-pipelines
  5. PySpark
  6. Delta
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Комментарии
Автор

hey buddy

please don't stop making videos. continue consistantly and your channel will really grow with time. you have good content

SMUser-sjdc
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Very nice mate. Can you please also mention how you installed Iceberg, for example, which version of the JAR file and where it is placed etc.?

Sayan_Mukherjee
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hello nice video. when have many parquet files in a datalake, what is the correct way to deal with them? read all of them and insert rows in an iceberg table o just link an iceberg table to all the parquet file?

ufiepte
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Iceberg is not. a fileformat, it is. a table format. The underlying file can still be in a Parquet format. Please don't give incorrect information.

BlueberretRam