Solution · Analytics delivery

Keep cloud warehouses synchronized with operations

Stream operational changes into Snowflake, BigQuery, object storage, and other analytical targets using snapshots, CDC, and destination-native bulk loading.

Explore warehouse sync Talk to our team
OPERATIONAL CHANGES → WAREHOUSE CHANGE EVENTS · SAME KEY, MANY OPS 8842 U 8842 U 9001 I 9310 U 8842 D 9001 U collapse by key ONE BATCH PER KEY 8842 D 9001 U 9310 U 6 events → 3 rows stage once STAGED ARTIFACT orders_20260905.parquet Parquet or CSV in object storage warehouse-native load Snowflake loaded COPY INTO analytics.orders
  1. 01 Capture operational changes
  2. 02 Combine and stage batches
  3. 03 Use warehouse-native loaders

The challenge

Fresh analytics should not overload operations

Warehouse pipelines need low-latency changes and high-throughput loads without turning production databases into reporting engines.

01 / Freshness

Reports lag behind operations

Nightly and hourly loads leave decision-makers waiting for changes that have already happened.

02 / Scale

Row-by-row writes cost too much

High change volumes create excessive round-trips and underuse the warehouse's native ingestion path.

03 / Complexity

Every target becomes a custom pipeline

Different staging formats, credentials, and write semantics multiply engineering and maintenance effort.

How it works

Capture continuously, load efficiently

Gluesync separates source capture from target application, then batches changes with the mechanism each analytical platform handles best.

  1. 01

    Ingest

    Capture operational changes

    Use native CDC readers and snapshots across databases without adding analytical queries to production workloads.

  2. 02

    Prepare

    Combine and stage batches

    Collapse multiple operations on the same key and prepare CSV or Parquet artifacts for efficient loading.

  3. 03

    Apply

    Use warehouse-native loaders

    Deliver through COPY, MERGE, Snowpipe, or cloud SDK load jobs according to the target connector.

Gluesync capabilities

Purpose-built paths into analytical targets

Move both baseline and incremental data while controlling schemas, mappings, and load behavior from one platform.

01

Snowflake delivery

Write through the Snowflake JDBC driver and Apache Arrow, with Snowpipe and COPY-based bulk workflows.

  • Snapshot and bulk-load support
  • RSA key or PAT authentication
  • Warehouse and staging configuration

Snowflake target

02

BigQuery delivery

Use the native BigQuery SDK with GCS staging for high-throughput snapshot and batch application.

  • Parquet or CSV staging
  • Regional dataset support
  • Service-account authentication

BigQuery target

03

Data-lake formats

Land snapshots and changes in analytics-friendly object storage formats for downstream processing.

  • Apache Parquet
  • Operation metadata for CDC
  • Timestamp-based object paths

Parquet support

Give analytics a fresher operational feed

Start a trial or discuss warehouse targets, loading patterns, and expected change volume with MOLO17.

Request a free trial Talk to our team