Canonical: https://molo17.com/solutions/data-lake/ Markdown mirror: https://molo17.com/solutions/data-lake/index.md Title: Data Lake solution · Gluesync · MOLO17 Description: Write snapshots and continuous changes into Amazon S3, Azure Data Lake Storage Gen2, and Google Cloud Storage as Parquet, CSV, or JSON—without adding analytical load to production databases. Solution · Lakehouse ingestion # Land operational changes in the data lake Write snapshots and continuous changes into Amazon S3, Azure Data Lake Storage Gen2, and Google Cloud Storage as Parquet, CSV, or JSON—without adding analytical load to production databases. [Explore data lake ingestion](#how-it-works) [Talk to our team](/contacts/?topic=data-architecture) 1. 01 Read changes at the source 2. 02 Write columnar batches 3. 03 Organize objects on write The challenge ## The bronze layer ages faster than it loads Lake ingestion has to move historical volume once and then keep landing changes, in a format downstream engines can read without another conversion step. 01 / Freshness ### Extracts land once a night Scheduled dumps leave the landing zone describing a version of the business that already moved on hours ago. 02 / Format ### Raw files need another pass Delimited exports force downstream jobs to convert, infer types, and re-partition before anything can be queried. 03 / Lineage ### Files lose the change context A flat row export does not record which transaction produced it, making audit, replay, and deduplication harder. How it works ## Snapshot once, then keep landing changes Gluesync captures at the source, batches what changed, and writes columnar files into object storage through each target agent's native SDK. 1. 01 Capture ### Read changes at the source Source agents use transaction logs, journals, and native change streams, so the lake feed does not scan production tables. 2. 02 Shape ### Write columnar batches Changes are serialized to Apache Parquet by default, with CSV and JSON selectable per entity. 3. 03 Land ### Organize objects on write Files are written under a folder path built from transaction type, table name, year, month, and timestamp. Gluesync capabilities ## Object storage targets ready for downstream engines Choose the object store, the file format, and the metadata each entity carries—from the same Core Hub that runs the rest of the estate. 01 ### Parquet by default Store replicated entities in Apache Parquet, selected per entity, with CSV and JSON as alternatives. - Per-entity format selection - MOLO17 ParquetKt writer - Folder-to-table object layout [Parquet and CSV support](https://docs.molo17.com/gluesync/latest/core-hub/parquet-files-support.html) 02 ### Cloud object-store agents Write batches through each provider's native SDK instead of a generic file interface. - Amazon S3 and S3-compatible buckets - Azure Data Lake Storage Gen2 - Google Cloud Storage [AWS S3 target](https://docs.molo17.com/gluesync/latest/agents/awss3-intro.html) [ADLS Gen2 target](https://docs.molo17.com/gluesync/latest/agents/azure-datalake-gen2-intro.html) 03 ### Change context on every record Enrich landed records with transaction metadata read from the source log during both snapshot and CDC phases. - Transaction and operation metadata - Underscore-prefixed technical columns - Per-pipeline toggles [Technical fields](https://docs.molo17.com/gluesync/latest/core-hub/technical-fields.html) Related solutions ## Continue exploring Gluesync use cases [ Solution · Analytics delivery ### Warehouse Sync Stream operational changes into Snowflake, BigQuery, object storage, and other analytical targets using snapshots, CDC, and destination-native bulk loading. Explore Warehouse Sync →](/solutions/warehouse-sync/)[ Solution · Real-time integration ### CDC Streaming Capture committed database changes from transaction logs and deliver them continuously to databases, event streams, and data platforms—without polling full tables. Explore CDC Streaming →](/solutions/cdc-streaming/)[ Solution · AI data supply ### AI & RAG Keep the stores behind retrieval and agent workflows synchronized with operational systems, and expose Core Hub pipelines and SQL to MCP clients under the caller's own permissions. Explore AI & RAG →](/solutions/ai-rag/) ## Keep the lake as current as the source Start a trial or talk with MOLO17 about object-store targets, file formats, and expected change volume. [Request a free trial](/get-gluesync/) [Talk to our team](/contacts/?topic=data-architecture)