Databricks-Understand File Formats Optimization #datascience #python #programming #jeenu #data #aws

Databricks is a cloud-based platform that offers various optimization features and keywords for working with different file formats efficiently. Here's an overview of some common file formats and their optimization features in Databricks:

Parquet File Format:

Optimization Features: Parquet is a columnar storage file format that efficiently stores and processes data. It is highly optimized for analytics workloads, offering features such as efficient compression, predicate pushdown, column pruning, and parallel processing. Keywords: When working with Parquet files in Databricks, you can use keywords such as parquet, spark.read.parquet(), and spark.write.parquet() to read from and write to Parquet files. Additionally, you can specify various options such as compression codecs (snappy, gzip, lzo), partitioning, and columnar storage options. Delta Lake File Format:

Optimization Features: Delta Lake is an open-source storage layer that brings ACID transactions, data versioning, and data lineage to Apache Spark and big data workloads. It offers features such as time travel, schema enforcement, data compaction, and automatic optimization. Keywords: When working with Delta Lake files in Databricks, you can use keywords such as delta, spark.read.format("delta"), and spark.write.format("delta") to read from and write to Delta Lake tables. You can also use Delta-specific commands like OPTIMIZE, VACUUM, and MERGE to optimize table performance, manage table history, and merge small files. CSV File Format:

Optimization Features: While CSV (Comma-Separated Values) is a common file format for storing tabular data, it may not be as optimized for analytics workloads as Parquet or Delta Lake. However, you can optimize CSV file processing by configuring options such as schema inference, header handling, and partitioning. Keywords: When working with CSV files in Databricks, you can use keywords such as csv, spark.read.csv(), and spark.write.csv() to read from and write to CSV files. You can specify options such as header, inferSchema, delimiter, quote, and escape to customize CSV file parsing. ORC File Format:

Optimization Features: ORC (Optimized Row Columnar) is another columnar storage file format that offers similar benefits to Parquet. It provides efficient compression, predicate pushdown, and support for complex data types, making it suitable for analytics workloads. Keywords: When working with ORC files in Databricks, you can use keywords such as orc, spark.read.orc(), and spark.write.orc() to read from and write to ORC files. You can also configure options such as compression codecs (snappy, zlib, lzo), predicate pushdown, and partitioning. AVRO File Format:

Optimization Features: AVRO is a binary serialization format that provides schema evolution, data compression, and efficient serialization/deserialization. It is suitable for storing complex data types and evolving schemas. Keywords: When working with AVRO files in Databricks, you can use keywords such as avro, spark.read.format("avro"), and spark.write.format("avro") to read from and write to AVRO files. You can specify options such as compression, schema, and recordName to customize AVRO file handling. Overall, Databricks provides various optimization features and keywords for working with different file formats, allowing users to efficiently store, process, and analyze data in their analytics workloads.

#Databricks, #DataAnalytics, #DataEngineering, #DataScience, #BigData, #CloudComputing, #ApacheSpark, #DataLake, #DataWarehouse, #ETL, #MachineLearning, #ArtificialIntelligence, #DataProcessing, #DeltaLake, #Parquet, #DataEngineering, #DataVisualization, #DataInsights, #DataOps ... https://www.youtube.com/watch?v=A8wxJ4i2HHA