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bruin-data avatar

bruin-data/ingestr

0
View on GitHub↗
3,714 stars·133 forks·Go·16 viewsgetbruin.com/docs/ingestr↗

Ingestr

ingestr is a command-line tool for copying and syncing data between different database engines and third-party platforms without writing custom code. It functions as an ETL pipeline utility that extracts data from diverse sources and loads it into destinations.

The tool features a schema-agnostic data loader that maps source fields to destination columns dynamically, removing the need for predefined static table definitions. It also operates as an incremental data synchronizer, updating destination tables by appending new records or merging changes to maintain current datasets.

The system provides capabilities for cross-database migration and cross-platform data transfer. It utilizes batch-based stream processing to move large volumes of data and employs an upsert strategy to maintain data consistency based on unique keys.

Features

  • Cross-Database Data Migrations - Moves data between disparate database engines and third-party platforms without requiring custom migration code.
  • Cross-Database Data Transfers - Moves data across various database engines and external platforms by streaming result sets.
  • Incremental Syncing - Updates destination tables by identifying new or modified records through primary keys to maintain source state.
  • ETL Workflows - Automates the extraction, transformation, and loading of data from diverse sources via a CLI.
  • Data Synchronization Pipelines - Maintains an accurate reflection of source data in a destination system through automated sync pipelines.
  • ETL Pipeline Tools - Functions as an ETL pipeline utility that extracts data from diverse sources and loads it into destinations.
  • Incremental Data Loading - Updates destination tables by processing only new or modified records to keep datasets current.
  • Incremental Data Synchronization - Fetches and merges only new data records generated since the last synchronization to avoid full re-loads.
  • Plugin-Based Data Source Adapters - Implements a pluggable adapter system to extract raw data from diverse databases and third-party APIs.
  • Schema-Agnostic Data Mappings - Maps source fields to destination columns dynamically without requiring predefined static table definitions.
  • Schema Column Mapping - Dynamically maps source fields to destination columns during data transfer between disparate systems.
  • Upsert Strategies - Combines inserting new records and updating existing ones based on a unique key to maintain data consistency.
  • Bulk Data Transfers via COPY - Moves large volumes of data between various databases and platforms using high-throughput transfer mechanisms.
  • Database Migration CLIs - Provides a command-line interface to automate data copying and syncing between different database engines.
  • Batch Data Processing - Efficiently moves large volumes of data between systems using bulk operations and small chunks.
  • Incremental Sync Checkpointings - Tracks the last processed record using markers to fetch only new or updated data since the last run.
  • Batch-Based Stream Processing - Processes large datasets in small chunks to maintain a low memory footprint during data transfer.
  • Schema-Agnostic Ingestion - Dynamically maps source fields to destination columns without requiring predefined static table definitions.
  • Upsert Operations - Implements atomic insert-or-update operations based on unique keys to maintain data consistency during transfers.
  • CLI-Driven Configurations - Drives the entire data pipeline through command-line arguments instead of external configuration files.
  • Data Ingestion - CLI utility for copying data between various sources.
  • Data Ingestion Pipelines - CLI tool for copying data between various sources.
  • Data Integration Providers - Copy data between various sources and destinations via CLI.
  • Data Pipelines - CLI tool for copying data between sources and destinations.
  • Developer Tools - CLI tool to copy data from any source to any destination.

Star history

Star history chart for bruin-data/ingestrStar history chart for bruin-data/ingestr

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with Ingestr

These projects share indexed features with Ingestr. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • dlt-hub/dltdlt-hub avatar

    dlt-hub/dlt

    5,472View on GitHub↗

    dlt is a Python data ingestion tool and ETL pipeline framework designed to fetch data from diverse sources and persist it into structured destinations. It functions as a schema inference engine that automatically detects data types and flattens nested JSON structures into relational tables, moving data from sources to lakehouses, warehouses, or vector databases. The project distinguishes itself through AI-powered pipeline generation, using large language models to scaffold extraction code and connectors for REST APIs. It also supports multimodal vector storage and specialized population of ve

    Pythondatadata-engineeringdata-lake
    View on GitHub↗5,472
  • bruin-data/bruinbruin-data avatar

    bruin-data/bruin

    1,620View on GitHub↗

    Build data pipelines with SQL and Python, ingest data from different sources, add quality checks, and build end-to-end flows.

    Goanalyticsbigquerydata-analysis
    View on GitHub↗1,620
  • alibaba/dataxalibaba avatar

    alibaba/DataX

    17,241View on GitHub↗

    DataX is a distributed data integration framework and plugin-based ETL tool designed for synchronizing large datasets between heterogeneous sources and destinations. It functions as a JDBC data migration engine and offline synchronization tool, enabling the movement of data between relational databases, NoSQL stores, and object storage. The system utilizes a plugin-based connector architecture that decouples reader and writer logic, allowing it to map and transform data types across different storage engines using a standardized internal representation. This design supports heterogeneous data

    Java
    View on GitHub↗17,241
  • gazette/coregazette avatar

    gazette/core

    793View on GitHub↗

    Build platforms that flexibly mix SQL, batch, and stream processing paradigms

    Go
    View on GitHub↗793
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Frequently asked questions

What does bruin-data/ingestr do?

ingestr is a command-line tool for copying and syncing data between different database engines and third-party platforms without writing custom code. It functions as an ETL pipeline utility that extracts data from diverse sources and loads it into destinations.

What are the main features of bruin-data/ingestr?

The main features of bruin-data/ingestr are: Cross-Database Data Migrations, Cross-Database Data Transfers, Incremental Syncing, ETL Workflows, Data Synchronization Pipelines, ETL Pipeline Tools, Incremental Data Loading, Incremental Data Synchronization.

Which projects share features with bruin-data/ingestr?

Projects with overlapping indexed features include: dlt-hub/dlt — dlt is a Python data ingestion tool and ETL pipeline framework designed to fetch data from diverse sources and persist… bruin-data/bruin — Build data pipelines with SQL and Python, ingest data from different sources, add quality checks, and build end-to-end… alibaba/datax — DataX is a distributed data integration framework and plugin-based ETL tool designed for synchronizing large datasets… gazette/core — Build platforms that flexibly mix SQL, batch, and stream processing paradigms. netflix/suro — Netflix's distributed Data Pipeline. rudderlabs/rudder-server — Rudder Server is a customer data platform and event routing pipeline designed to collect, transform, and route…