Bhakti-Aarti- Android app Privacy policy

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 Privacy Policy Effective Date: October 4th, 2025   App Name: Bhakti Aarti   Developer: k3kventures   Contact: sultanmirzadev@gmail.com --- 1. Introduction This Privacy Policy explains how we collect, use, and protect your personal information when you use our Android application "Bhakti Aarti" ("App"). By using the App, you agree to the terms of this Privacy Policy. --- 2. Information We Collect a. Voice Input Our App may request access to your device’s microphone in order to enable voice-based search functionality. When you use this feature: - The app listens only when you activate the voice input manually.   - Your voice input is processed to understand your search query.   - We do not store or share your voice recordings or voice data. Voice data may be processed by Google's speech recognition services (or similar services) if you are using a device with Google Play Services enabled. Please refer to Google’s Privacy Policy at:   https://policies.go...
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Benefits of Apache Parquet Format in big fata

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Benefits of Parquet Format

  1. Columnar Storage

    • Efficient for analytics and read-heavy workloads.
    • Only required columns are read into memory.

  2. Highly Compressed

    • Supports efficient compression algorithms (Snappy, GZIP, Brotli).
    • Smaller file size compared to row-based formats like CSV/JSON.

  3. Splittable & Scalable

    • Files can be split and read in parallel, improving speed in distributed systems like Hadoop/Spark.

  4. Schema Evolution

    • Supports adding new columns without breaking existing data pipelines.

  5. Efficient for Queries

    • Works well with SQL engines like Hive, Presto, Trino, Athena, BigQuery.

  6. Better IO Performance

    • Reduces disk and network IO by avoiding unnecessary data reads.

  7. Interoperable

    • Supported across multiple languages and platforms (Python, Java, Spark, Hive, AWS, GCP, etc.).

  8. Self-describing Format

    • Stores schema as metadata within the file itself — no need for external schema definitions.

  9. Great with Partitioning

    • When used with tools like Hive/Spark, supports directory-based partitioning, improving query performance.

  10. Ideal for Lakehouse/Data Lake

  • Common choice for Delta Lake, Iceberg, Hudi — supports ACID on Parquet.

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