Data Quality Checks in Python

Data Lake vs Delta Lake vs Data Warehouse

Data Lake

Aspect	Description
Definition	A centralized repository that allows you to store all your structured and unstructured data at any scale.
Key Features	✔️ Stores all types of data (no schema enforcement). ✔️ Built on cheap object storage (S3, ADLS, HDFS). ✔️ Supports batch & streaming processing (Spark, Flink). ❌ No ACID transactions (risk of dirty reads/writes). ❌ No built-in indexing (slow for analytics).
Data Types	Supports all types of data including raw, unprocessed data (e.g., logs, images, videos).
Schema	Schema-on-read: Data is stored in its raw format and schema is applied when the data is read.
Use Cases	Big data analytics (Hadoop, Spark), machine learning (raw data storage), data exploration, Log & event data archiving.
Examples	Amazon S3, Azure Data Lake Storage, Google Cloud Storage.

Delta Lake

Aspect	Description
Definition	An open-source storage layer that brings ACID (Atomicity, Consistency, Isolation, Durability) transactions to data lakes, built on top of Apache Parquet. It allows schema enforcement and, time travel to data lakes.
Key Features	✔️ Adds ACID transactions to data lakes (ensures data integrity). ✔️ Supports schema evolution & enforcement (prevents bad data). ✔️ Provides time travel (query historical data versions). ✔️ Built on Parquet format (efficient storage & compression). ✔️ Integrates with big data tools (Spark, Presto, Hive). ❌ Not a standalone DB (needs Spark/Flink)..
Data Types	Structured and semi-structured data (tables, JSON, CSV).
Schema	Schema-on-write: Enforces schema when data is written, but allows schema evolution.
Use Cases	Data engineering (ETL pipelines), machine learning (feature store), real-time analytics, streaming data processing.
Examples	Delta Lake on Databricks, Delta Lake on AWS, Delta Lake on Azure.

Data Warehouse

Aspect	Description
Definition	A centralized repository designed for reporting and data analysis, optimized for read-heavy operations and complex queries.
Key Features	✔️ Optimized for complex queries and analytics (OLAP). ✔️ Enforces a strict schema (schema-on-write). ✔️ Supports indexing and partitioning (improves query performance). ✔️ Provides data governance and security features. ❌ Expensive storage (compared to data lakes). ❌ Not ideal for unstructured data.
Data Types	Primarily structured data (tables, relational data).
Schema	Schema-on-write: Data must conform to a predefined schema before being loaded.
Use Cases	Business intelligence (BI) reporting, historical data analysis, complex queries, data aggregation.
Examples	Amazon Redshift, Google BigQuery, Snowflake. SQL Server, Oracle, MySQL.

Data Lakehouse

Aspect	Description
Definition	A modern data architecture that combines the benefits of data lakes and data warehouses, enabling both structured and unstructured data processing with ACID transactions.
Key Features	✔️ Combines the flexibility of data lakes with the performance of data warehouses. ✔️ Supports ACID transactions (data integrity). ✔️ Enables both batch and real-time processing. ✔️ Allows schema evolution and enforcement. ✔️ Optimized for machine learning and analytics workloads. ❌ Relatively new concept (less mature ecosystem).
Data Types	Supports all types of data (structured, semi-structured, unstructured).
Schema	Flexible schema: Supports both schema-on-read and schema-on-write.
Use Cases	Unified analytics, machine learning, real-time analytics, data science workloads.
Examples	Databricks Lakehouse, Snowflake, Apache Hudi, Apache Iceberg.

Comparison Table

Aspect	Data Lake	Delta Lake	Data Warehouse	Data Lakehouse
Definition	Centralized repository for all types of data at any scale.	Storage layer that adds ACID transactions to data lakes.	Centralized repository optimized for reporting and analysis.	Combines benefits of data lakes and warehouses with ACID transactions.
Key Features	Stores all data types, cheap storage, batch & streaming processing, no ACID, no indexing.	ACID transactions, schema enforcement, time travel, Parquet-based, integrates with big data tools.	Optimized for complex queries, strict schema, indexing, data governance, expensive storage.	Flexibility of lakes + performance of warehouses, ACID, batch & real-time processing, schema evolution.
ACID	❌ No	✔️ Yes	✔️ Yes	✔️ Yes
Performance	Slow (Full Scan)	Improved (Indexing, Caching)	Fast (Optimized for Queries)	Fast (Optimized for Analytics)
Cost	Cheap (Object Storage)	Moderate (Storage + Compute)	Expensive (Optimized Storage)	Moderate to High (Depends on Implementation)
Data Types	All types (structured & unstructured).	Structured & semi-structured (tables, JSON, CSV).	Primarily structured (relational data).	All types (structured, semi-structured, unstructured).
Schema	Schema-on-read.	Schema-on-write with evolution support.	Schema-on-write.	Flexible schema: both schema-on-read & schema-on-write.
Use Cases	Big data analytics, machine learning (raw data), data exploration, log archiving.	Data engineering (ETL), machine learning (feature store), real-time analytics.	BI reporting, historical analysis, complex queries, data aggregation.	Unified analytics, machine learning, real-time analytics, data science.
Examples	Amazon S3, Azure Data Lake Storage, Google Cloud Storage.	Delta Lake on Databricks, AWS, Azure.	Amazon Redshift, Google BigQuery, Snowflake, SQL Server, Oracle.	Databricks Lakehouse, Snowflake, Apache Hudi, Apache Iceberg.
Best for	ML, raw storage	Data engineering	BI, complex queries	Unified analytics

When to Use Which?

• Data Lake: Low Cost; When you need to store vast amounts of raw, unstructured data for future processing or exploration.
• Delta Lake: Reliability & Transactions; When you want to add reliability and ACID transactions to your existing data lake for better data integrity.
• Data Warehouse: Fast SQL Analytics; When your primary need is for structured data analysis, reporting, and complex queries with high performance.
• Data Lakehouse: Combination of Raw with Analytics; When you want a unified platform that supports both structured and unstructured data with ACID transactions and real-time analytics capabilities.

Key Takeaways

• Dump everything here. Data Lakes are great for storing all types of data cheaply but lack ACID transactions and indexing.
• Data Lake with reliability. Delta Lake enhances data lakes with ACID transactions, schema enforcement, and time travel capabilities.
• Clean data for fast SQL. Data Warehouses are optimized for structured data analysis with strict schemas and high performance but can be costly.
• Best of both worlds. Data Lakehouses combine the flexibility of data lakes with the performance of data warehouses, supporting diverse data types and workloads.
• Choose based on needs. Select the right architecture based on your specific use cases, data types, and performance requirements.

There are real-world scenarios where Data Lake, Delta Lake, and Data Warehouse work together in a unified architecture.

Here’s a practical use case where all three are needed:

Use Cases: Unified Analytics Platform for an E-Commerce Company

Goal: Build a scalable, end-to-end data pipeline that handles raw data ingestion, reliable transformations, and business-friendly analytics.

1. Data Lake (Raw Storage)

• What: Store all raw data from various sources in native formats.
• Data Examples:
  • Clickstream logs (JSON)
  • Product images (JPEG, PNG)
  • Customer reviews (CSV)
  • Transaction records (Parquet)
  • IoT sensor data (CSV/Parquet)
  • Social media feeds (APIs → JSON)
• Why Needed:
  • Cheap storage for large volumes of diverse data.
  • Flexibility to store unstructured and semi-structured data.
  • Foundation for future processing and exploration.
• Technology: Use Amazon S3 or Azure Data Lake Storage to store raw, or HDFS unstructured data in its native format (JSON, CSV, Parquet).

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2. Delta Lake (Reliable Processing Layer)

• What: Adds structure, reliability, and ACID compliance to the Data Lake.
• Use Cases:
  • CDC (Change Data Capture): Merge real-time order updates from databases.
  • Schema Enforcement: Ensure clean data before loading to the warehouse.
  • Time Travel: Debug issues by querying past versions (e.g., "Why did sales drop on Day X?").
• Why Needed:
  • Prevents "data swamp" issues (bad quality, duplicates).
  • Prevents bad data with schema enforcement.
  • Enables historical data analysis with time travel.
  • Enables batch + streaming in one system (e.g., Spark + Kafka).
• Technology: Delta Lake on Databricks / Delta Lake Standalone on AWS / Azure.

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3. Data Warehouse (Business Analytics)

• What: Optimized SQL layer for reporting, dashboards, and ad-hoc queries.
• Use Cases:
  • BI Reports: "Top 10 products by revenue last quarter."
  • Customer Segmentation: SQL queries for marketing teams.
  • Regulatory Compliance: Auditable, governed data.
• Why Needed:
  • Fast performance for complex JOINs/aggregations.
  • Self-service for business users (Tableau, Power BI).
  • Data governance and security features.
  • Optimized storage for structured data.
• Technology: Snowflake / BigQuery / Redshift

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How They Work Together

• Data Lands in Data Lake (e.g., raw JSON logs from apps).
• Delta Lake Processes It (clean, deduplicate, enforce schema).
• Curated Data Moves to Data Warehouse (for BI/analytics).
• Business Users Query the Warehouse (fast, reliable reports).

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Real-World Example: Fraud Detection Pipeline

• Data Lake : Stores raw transaction logs (some fraudulent, some legit).
• Delta Lake :
  • Runs Spark ML to flag suspicious transactions.
  • Uses ACID transactions to update fraud labels.
• Data Warehouse :
  • Executes fast SQL queries for fraud dashboards.
  • Alerts analysts via Tableau.

PySpark vs Spark SQL

PySpark vs. Spark SQL: Which is Better?

When working with Apache Spark, you have two primary options for performing joins and transformations: PySpark DataFrame API and Spark SQL. Both approaches have their strengths and weaknesses, and the choice depends on your use case, familiarity, and performance requirements.

1. PySpark DataFrame API

The PySpark DataFrame API allows you to perform transformations and joins programmatically using Python. It is a functional and expressive way to work with data in Spark.

Advantages:

• Programmatic Control: You can dynamically build transformations and joins using Python code.
• Intermediate Transformations: Easily perform intermediate transformations between joins.
• Type Safety: PySpark provides type safety and autocompletion in IDEs, reducing errors.

Disadvantages:

• Verbose Syntax: Complex joins can become verbose and harder to read.
• No Multi-DataFrame Joins: You cannot join multiple DataFrames in a single shot; chaining is required.

Example:

# PySpark DataFrame API
final_df = (
    countries_region_df
    .join(countries_df_hub, countries_region_df["country_id"] == countries_df_hub["country_key"], "inner")
    .join(region_df_hub, countries_region_df["region_id"] == region_df_hub["region_key"], "inner")
)
    

2. Spark SQL

Spark SQL allows you to write SQL-like queries to perform joins and transformations. It is ideal for users familiar with SQL and for complex queries.

Advantages:

• SQL-Like Syntax: Easier to write and read for users familiar with SQL.
• Multi-DataFrame Joins: You can join multiple DataFrames in a single query.
• Optimized Execution: Spark SQL often generates optimized execution plans.

Disadvantages:

• Less Programmatic Control: Harder to dynamically build queries or perform intermediate transformations.
• String-Based Queries: Queries are written as strings, which can lead to runtime errors.

Example:

# Spark SQL
countries_region_df.createOrReplaceTempView("countries_region")
countries_df_hub.createOrReplaceTempView("countries_hub")
region_df_hub.createOrReplaceTempView("region_hub")

final_df = spark.sql("""
    SELECT *
    FROM countries_region cr
    JOIN countries_hub ch ON cr.country_id = ch.country_key
    JOIN region_hub rh ON cr.region_id = rh.region_key
""")
    

3. Comparison: PySpark vs. Spark SQL

Feature	PySpark DataFrame API	Spark SQL
Syntax	Programmatic, Python-based	SQL-like
Multi-DataFrame Joins	Not supported directly; requires chaining	Supported in a single query
Readability	Can become verbose for complex joins	Easier to read for SQL users
Performance	Depends on join order and optimizations	Often generates optimized execution plans
Flexibility	High (programmatic control)	Limited (string-based queries)

4. Which One Should You Use?

The choice between PySpark and Spark SQL depends on your use case:

• Use PySpark DataFrame API if:
  • You need programmatic control over transformations.
  • You are performing intermediate transformations between joins.
  • You prefer Python over SQL.
• Use Spark SQL if:
  • You are familiar with SQL and prefer its syntax.
  • You are performing complex joins involving multiple DataFrames.
  • You want optimized execution plans.

5. Conclusion

Both PySpark DataFrame API and Spark SQL are powerful tools for working with data in Apache Spark. While PySpark offers more flexibility and programmatic control, Spark SQL provides a more familiar and optimized approach for SQL users. Choose the one that best fits your workflow and use case.

Different Types of File Formats

1. Row-Based Formats

These formats store data row by row, making them ideal for transactional workloads.

Format	Description	Use Case	Pros	Cons
CSV	Plain text format where each line represents a row, and values are separated by commas.	Simple data exchange, small datasets, human-readable format.	Easy to read and write, widely supported.	No compression, no schema support, inefficient for large datasets.
TSV	Similar to CSV but uses tabs as delimiters.	Same as CSV, but better for data containing commas.	Easy to read and write.	No compression, no schema support.
JSON	A lightweight, human-readable format for storing structured data.	Web APIs, configuration files, semi-structured data.	Human-readable, supports nested structures.	No compression, inefficient for large datasets.
XML	A markup language for storing structured data.	Document storage, configuration files, web services.	Human-readable, supports complex structures.	Verbose, no compression, inefficient for large datasets.
EBCDIC	EBCDIC (Extended Binary Coded Decimal Interchange Code) is a character encoding system primarily used on IBM mainframe and midrange computer systems. It differs from ASCII, which is more commonly used on modern systems like Windows and Unix-based platforms.	Legacy systems, mainframe data processing.	Compatible with IBM systems, supports a wide range of characters.	Less common, limited support in modern systems.

2. Column-Based Formats

These formats store data column by column, making them ideal for analytical workloads.

Format	Description	Use Case	Pros	Cons
Avro	A binary format that includes schema information for data serialization.	Data serialization, big data processing.	Compact, supports schema evolution.	Less human-readable, requires schema management.
Parquet	A columnar storage format optimized for large-scale data processing.	Big data analytics, data warehousing.	Highly efficient for read-heavy workloads, supports complex data types.	Less efficient for write-heavy workloads, requires more setup.
ORC	A columnar storage format designed for Hadoop workloads.	Big data analytics, data warehousing.	Highly efficient for read-heavy workloads, supports complex data types.	Less efficient for write-heavy workloads, requires more setup.

3. Binary Formats

These formats store data in a binary format, making them efficient for both storage and processing.

Format	Description	Use Case	Pros	Cons
Protocol Buffers (Protobuf)	A language-neutral, platform-neutral binary serialization format developed by Google.	Data serialization, inter-service communication.	Compact, fast, supports schema evolution.	Requires schema definition, less human-readable.
MessagePack	A binary format that is more compact than JSON but still human-readable.	Data serialization, web APIs.	Compact, faster than JSON.	Less human-readable than JSON, limited support.
CBOR	A binary format designed for small code size and message size, suitable for constrained environments.	IoT, embedded systems.	Compact, efficient for constrained environments.	Less human-readable, limited support.
Thrift	The Apache Thrift framework utilizes a specific file format, known as the Thrift Interface Definition Language (IDL), to define data types and service interfaces. This definition file, typically with a .thrift extension, serves as a language-agnostic blueprint for generating code across various programming languages.	Data serialization, inter-service communication.	Compact, supports multiple programming languages.	Requires schema definition, less human-readable.

4. Specialized Formats

These formats are designed for specific use cases, such as time-series data or geospatial data.

Format	Description	Use Case	Pros	Cons
Feather	A binary columnar format designed for fast data interchange between Python and R.	Data science, in-memory analytics.	Fast read/write, easy to use with pandas and R.	Limited support outside Python and R ecosystems.
HDF5	A hierarchical data format for storing large amounts of numerical data.	Scientific computing, large datasets.	Supports complex data structures, efficient storage.	Less human-readable, requires specific libraries.
NetCDF	A format for array-oriented scientific data, commonly used in meteorology and oceanography.	Scientific computing, large datasets.	Supports complex data structures, efficient storage.	Less human-readable, requires specific libraries.
Arrow	A cross-language development platform for in-memory data, designed for high-performance analytics. It provides a standardized columnar memory format that enables efficient data interchange between different systems and languages.	Data science, in-memory analytics.	Fast read/write, supports multiple languages.	Requires specific libraries, less human-readable.

5. Compressed Formats

These formats use compression techniques to reduce file size, making them ideal for storage and transfer.

Format	Description	Use Case	Pros	Cons
Gzip	A widely used compression format that reduces file size using the DEFLATE algorithm. It is commonly used for compressing text files, such as CSV and JSON, to save storage space and improve transfer speeds.	File compression, web content delivery.	High compression ratio, widely supported.	Slower compression/decompression, not suitable for random access.
Bzip2	Another widely used compression format that reduces file size using the Burrows-Wheeler algorithm. It typically achieves better compression ratios than Gzip, but at the cost of slower compression and decompression speeds.	File compression, archival storage.	Better compression ratio than Gzip.	Slower compression/decompression, not suitable for random access.
LZ4	A high-speed compression format that prioritizes speed over compression ratio. It is often used in scenarios where fast compression and decompression are critical, such as real-time data processing.	Real-time data processing, in-memory compression.	Very fast compression/decompression.	Lower compression ratio than Gzip and Bzip2.
Snappy	A compression format developed by Google that focuses on speed and simplicity. It is commonly used in big data processing frameworks like Apache Hadoop and Apache Spark.	Big data processing, in-memory compression.	Fast compression/decompression, simple implementation.	Lower compression ratio than Gzip and Bzip2.

6. Database-Specific Formats

These formats are optimized for specific database systems.

Format	Description	Use Case	Pros	Cons
SQLite	A self-contained, serverless, zero-configuration, transactional SQL database engine. It is widely used in embedded systems and mobile applications due to its simplicity and lightweight nature.	Embedded databases, mobile applications, small-scale applications.	Lightweight, easy to use, no server required, ACID transactions, schema evolution, time travel.	Not suitable for large-scale applications.
BSON	MongoDB; A binary representation of JSON-like documents.	NoSQL databases (e.g., MongoDB).	Supports complex data types, efficient storage.	Larger than JSON, less human-readable.

7. Table Formats

These formats are designed to manage large datasets in a tabular format, often with support for ACID transactions and schema evolution.

Format	Description	Use Case	Pros	Cons
Apache Iceberg	A table format for managing large datasets in data lakes.	Data lakehouses, schema evolution, time travel.	ACID transactions, schema evolution, time travel, partitioning, and versioning.	Requires specific query engines for full functionality and adds complexity to data architecture.
Apache Hudi	A data lake table format that supports ACID transactions and incremental data processing.	Data lakehouses, incremental data processing.	ACID transactions, schema evolution, time travel, incremental processing.	Requires specific query engines for full functionality and adds complexity to data architecture.
Delta Lake	An open-source storage layer that brings ACID transactions to data lakes.	Data lakehouses, ACID transactions, schema evolution.	ACID transactions, schema evolution, time travel, unified batch and streaming.	Requires specific query engines for full functionality and adds complexity to data architecture.

8. Log Formats

These formats are designed for storing log data, often with support for time-series data.

Format	Description	Use Case	Pros	Cons
Logfmt	A simple, human-readable log format that uses key-value pairs to represent log entries.	Application logs, system logs.	Human-readable, easy to parse.	No compression, limited structure.
Syslog	A standard for message logging in an IP network, commonly used for system and application logs.	System logs, network device logs.	Widely supported, standardized format.	Limited structure, no compression.
CEF	Common Event Format (CEF) is a log management standard created by ArcSight. It provides a consistent format for event logs from various security devices and applications, making it easier to aggregate and analyze security data.	Security logs, SIEM systems.	Standardized format for security events.	Less human-readable, limited adoption outside security.

Summary Table

Format Type	Example Formats	Use Case	Pros	Cons
Row-Based	CSV, TSV, JSON, XML	Simple data exchange, small datasets, human-readable format.	Human-readable, easy to parse.	No compression, limited structure.
Column-Based	Parquet, ORC, Avro	Analytics, big data processing	High compression, efficient for queries	Not human-readable, slower for row ops.
Binary	Protobuf, Thrift	Data serialization, communication	Compact, fast, schema support	Not human-readable, requires schema
Specialized	Feather, Arrow, HDF5, NetCDF	Scientific data, in-memory analytics	High performance, supports large datasets	Complex to use, not general-purpose
Compressed	Gzip, Bzip2, LZ4, Snappy	File compression, storage optimization	Reduces file size, faster transfer	Slower access, not random-accessible
Database-Specific	SQLite, BSON	Embedded DBs, NoSQL databases	Optimized for specific DBs, ACID support	Limited to specific systems
Table Formats	Iceberg, Hudi, Delta Lake	Data lakehouses, large datasets	ACID transactions, schema evolution	Requires specific query engines
Log Formats	Logfmt, Syslog, CEF	Application logs, system logs	Human-readable, easy to parse	No compression, limited structure

Conclusion

The choice of file format depends on various factors, including the specific use case, data characteristics, and performance requirements. Understanding the strengths and weaknesses of each format can help in making an informed decision for data storage and processing needs.

• Use row-based formats (e.g., CSV, JSON) for simple data exchange and small datasets.
• Use column-based formats (e.g., Parquet, ORC) for analytics and big data processing.
• Use binary formats (e.g., Protobuf, Thrift) for efficient data serialization.
• Use specialized formats (e.g., Feather, HDF5) for scientific or in-memory data.
• Use table formats (e.g., Iceberg, Delta Lake) for managing large datasets in data lakes.

Parquet (Columnar Storage Format) /vs/ ORC (Optimized Row Columnar)

ORC vs Parquet Comparison

1. Overview

Feature	ORC	Parquet
Developed By	Apache Hive (part of the Hadoop ecosystem)	Apache Parquet (part of the Apache ecosystem)
Storage Format	Columnar	Columnar
Compression	Highly compressed (e.g., ZLIB, Snappy)	Highly compressed (e.g., Snappy, GZIP)
Schema Evolution	Limited support	Limited support
ACID Transactions	Supported (via Hive)	Not supported
Use Case	Hadoop ecosystem, Hive	General-purpose, cross-platform

2. Key Differences

a. Compression

• ORC: Uses advanced compression techniques (e.g., ZLIB, Snappy). Typically achieves higher compression ratios compared to Parquet.
• Parquet: Also uses advanced compression techniques (e.g., Snappy, GZIP). Compression ratios are slightly lower than ORC but still highly efficient.

b. Performance

• ORC: Optimized for Hive and the Hadoop ecosystem. Faster for Hive queries due to native integration.
• Parquet: Optimized for general-purpose big data processing. Faster for Spark and other non-Hive tools.

c. Schema Evolution

• ORC: Limited support for schema evolution. Adding or renaming columns requires rewriting the entire dataset.
• Parquet: Also has limited support for schema evolution. Adding or renaming columns requires rewriting the entire dataset.

d. ACID Transactions

• ORC: Supports ACID transactions when used with Hive. Enables features like updates, deletes, and merges.
• Parquet: Does not support ACID transactions. Primarily used for append-only workloads.

e. Ecosystem Integration

• ORC: Tightly integrated with Hive and the Hadoop ecosystem. Less support in non-Hadoop tools.
• Parquet: Widely supported across multiple platforms (e.g., Spark, Presto, Hive). More versatile for cross-platform use cases.

3. Use Cases

ORC

• Hadoop Ecosystem: Ideal for Hive-based data warehouses.
• ACID Transactions: Use cases requiring updates, deletes, and merges (via Hive).
• High Compression: Scenarios where storage efficiency is critical.

Parquet

• General-Purpose Analytics: Ideal for big data processing with tools like Spark, Presto, and Hive.
• Cross-Platform Use: Use cases requiring compatibility across multiple platforms.
• Append-Only Workloads: Scenarios where data is primarily appended (e.g., log data).

4. Pros and Cons

ORC

Pros	Cons
High compression ratios	Limited support outside the Hadoop ecosystem
ACID transactions with Hive	Limited schema evolution capabilities
Optimized for Hive queries	Less versatile for cross-platform use

Parquet

Pros	Cons
Widely supported across platforms	No ACID transaction support
Efficient for general-purpose analytics	Limited schema evolution capabilities
Open-source and free	Slightly lower compression ratios than ORC

5. Summary

Feature	ORC	Parquet
Compression	Higher compression ratios	Slightly lower compression ratios
Performance	Optimized for Hive	Optimized for Spark and general-purpose
ACID Transactions	Supported (via Hive)	Not supported
Ecosystem	Hadoop ecosystem	Cross-platform
Use Case	Hive-based data warehouses	General-purpose analytics

6. Conclusion

• Use ORC if you're working in the Hadoop ecosystem, especially with Hive, and need ACID transactions.
• Use Parquet if you need a general-purpose, cross-platform columnar storage format for big data analytics.

CSV (Comma-Separated Values) /vs/ Parquet (Columnar Storage Format)

Parquet  and CSV are two common file formats used for storing and processing data. Each has its own strengths and weaknesses, and the choice between them depends on your specific use case. Below is a detailed comparison of Parquet and CSV:

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1. Overview

Feature	CSV (Comma-Separated Values)	Parquet (Columnar Storage Format)
Format	Row-based (stores data row by row)	Columnar (stores data column by column)
Compression	Limited or no compression	Highly compressed (e.g., Snappy, GZIP, ZSTD)
Schema	Schema-less (no metadata about data types)	Schema-aware (stores metadata about data types)
Read Performance	Slower for large datasets (reads entire rows)	Faster for large datasets (reads only needed columns)
Write Performance	Faster for small datasets	Slower for small datasets (due to compression and columnar storage)
Storage Efficiency	Less efficient (stores data as plain text)	Highly efficient (compressed and columnar storage)
Use Case	Simple data exchange, small datasets, human-readable	Big data processing, analytics, large datasets

 

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2. Key Differences

 

	CSV (Comma-Separated Values)	Parquet (Columnar Storage Format)
Storage Format	Row-based storage: Each row is stored as a line of text, with values separated by commas (or other delimiters). Human-readable: Easy to view and edit using text editors or spreadsheet software. No metadata: Does not store information about data types or schema.	Columnar storage: Data is stored column by column, which is more efficient for analytical queries. Binary format: Not human-readable, optimized for machine processing. Schema-aware: Stores metadata about data types, making it self-describing.
Compression	Typically uncompressed or uses basic compression (e.g., GZIP). Larger file sizes compared to Parquet.	Highly compressed: Uses advanced compression algorithms (e.g., Snappy, GZIP, ZSTD). Smaller file sizes, reducing storage costs and improving I/O performance.
Performance	Slower for analytical queries: Reads entire rows, even if only a few columns are needed. Suitable for small datasets or simple data exchange.	Faster for analytical queries: Reads only the required columns, reducing I/O. Optimized for big data processing and analytics.
Schema Evolution	Schema-less: Changes in schema (e.g., adding/removing columns) require manual handling. No support for complex data types (e.g., nested structures).	Schema-aware: Supports schema evolution (e.g., adding/removing columns without rewriting the entire dataset). Supports complex data types (e.g., arrays, maps, nested structures).
Use Cases	Simple data exchange between systems. Small datasets or prototyping. Human-readable format for manual inspection.	Big data processing and analytics. Large datasets with complex schemas. Efficient storage and querying in distributed systems (e.g., Hadoop, Spark).

 

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3. Pros and Cons

CSV

Pros	Cons
Simple and human-readable	No compression (large file sizes)
Easy to create and edit	No schema or metadata support
Supported by almost all tools and systems	Slow for large datasets and complex queries
Suitable for small datasets	Limited support for complex data types

 

Parquet

Pros	Cons
Highly compressed (small file sizes)	Not human-readable
Columnar storage (fast for analytics)	Slower to write (due to compression)
Schema-aware (supports complex data types)	Requires tools/libraries to read/write
Optimized for big data processing	Overhead for small datasets

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4. When to Use CSV

• Small datasets : When working with small datasets that don't require advanced compression or performance optimizations.
• Human-readable format : When you need to manually inspect or edit the data.
• Simple data exchange : When exchanging data with systems that only support CSV.
• Prototyping : When quickly prototyping or testing data pipelines.

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5. When to Use Parquet

• Big data processing : When working with large datasets in distributed systems (e.g., Hadoop, Spark).
• Analytical queries : When performing analytical queries that require reading specific columns.
• Storage efficiency : When you need to reduce storage costs and improve I/O performance.
• Complex data types : When working with nested or complex data structures.

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6. Example Use Cases

CSV

• Exporting data from a database for manual analysis.
• Sharing small datasets with non-technical users.
• Loading data into a spreadsheet or simple database.

Parquet

• Storing large datasets in a data lake or data warehouse.
• Running analytical queries on big data platforms (e.g., Spark, Hive).
• Optimizing storage and query performance in distributed systems.

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7. Example Code

Reading and Writing CSV in PySpark

Spark Sample Code (CSV)

from pyspark.sql import SparkSession   # Initialize Spark session spark = SparkSession.builder.appName("CSV Example").getOrCreate()   # Read CSV df_csv = spark.read.csv("data.csv", header=True, inferSchema=True)   # Write CSV df_csv.write.csv("output.csv", header=True)

 

 

 

 

 

 

 

 

Reading and Writing Parquet in PySpark

Spark Sample Code (Parquet)

from pyspark.sql import SparkSession   # Initialize Spark session spark = SparkSession.builder.appName("Parquet Example").getOrCreate()   # Read Parquet df_parquet = spark.read.parquet("data.parquet")   # Write Parquet df_parquet.write.parquet("output.parquet")

 

 

 

 

 

 

 

 

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8. Summary

Feature	CSV	Parquet
Best For	Small datasets, human-readable format	Big data, analytics, storage efficiency
Compression	Limited or none	Highly compressed
Schema	Schema-less	Schema-aware
Performance	Slower for large datasets	Faster for analytical queries
Complex Data Types	Not supported	Supported

 

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Conclusion

• Use CSV for small datasets, simple data exchange, or when human readability is important.
• Use Parquet for big data processing, analytical queries, and efficient storage in distributed systems.