Parquet, CSV, JSON, and JSONL
Write to AWS Glue in the format your engine expects - compact, columnar Parquet for fast analytics on Athena, Trino, Spark, and BigQuery, plus row-based CSV, JSON, and JSONL for interchange any tool can read.
AWS Glue
Object Storage & Data Lake
Every source into AWS Glue, in open file formats.
Dataddo is the turnkey data layer for AWS Glue. Land data from 400+ business sources into Iceberg tables registered in the Glue Data Catalog - governed and analytics-ready - so Athena, Redshift, and EMR query them straight away. No pipelines to build.
ARCHITECTURE
Where AWS Glue fits in your data architecture
Sources
Business / DB / File / Streaming Connectors
450+ available, any direction
HubSpot
Google Ads
Google Sheets
Salesforce
SAP S/4HANA
Oracle
Microsoft Dynamics 365
AWS S3
Azure SQL
Google Cloud SQL
MySQL
PostgreSQL
IBM Db2
+ any other
Your existing stack
Orchestration
Monitoring
Governance & Lineage
IAM & SSO
Dataddo Platform
Speed
Security
Governance
Control Plane
UI
Visual workspace for teams to build, run and monitor pipelines
API
Programmatic interface to embed Dataddo in your own stack and workflows
MCP
Dedicated interface for AI & agents to access governed data in context
Data Plane
Isolated deployment
Hyperscalers
AWSAzureGoogle Cloud
Isolated deployment
EU Cloud Providers
HetznerOVHcloudSTACKIT
Isolated deployment
On-Prem
KubernetesOpenShift
Data + Metadata
Destinations
DWH / Data Lake / Lakehouse
Snowflake
Databricks
Microsoft Fabric
Google BigQuery
Amazon Redshift
Azure Synapse
Oracle
IBM Db2
Universal PostgreSQL
Universal MySQL
+ any other
Data + Metadata
Consumption
AI & Agents / Analytics
Claude
Gemini
OpenAI
Mistral
BottleCap AI
Power BI
Tableau
Looker Studio
Databox
+ any other
Sources
Business / DB / File / Streaming Connectors
450+ available, any direction
Orchestration
Monitoring
Governance & Lineage
IAM & SSO
Dataddo Platform
Speed
Security
Governance
Control Plane
Data Plane
Destinations
DWH / Data Lake / Lakehouse
Consumption
AI & Agents / Analytics
ETLELTReverse ETLCDCData Streaming
Any direction, any workload
OPEN FORMATS & PARTITIONING
Land AWS Glue data in open formats, partitioned for query
Every flow run writes governed data to AWS Glue as an open file - columnar Parquet for analytics, or row-based CSV, JSON, and JSONL for interchange - laid out in date-based partitions so engines like Athena, Trino, BigQuery, and Spark read it as a clean dataset and scan only what they need.
400+ managed connectors
Marketing, sales, finance, product, and ad platforms - plus databases and flat files - all maintained for you and ready to land in AWS Glue out of the box.
Date-partitioned datasets
Each run lands as its own dated file, so AWS Glue is organized as a partitioned dataset and query engines prune to just the files they need.
Snapshot or latest-state
Keep every run as a dated snapshot, or keep only the latest file - the file-naming strategy is your state management, chosen per flow.
Load the full history
Backfill historical date ranges into AWS Glue on demand - seed a new lake with everything you have, or reload a range to fill a gap.
Clean, governed data
Blend and reshape sources, then let the Data Quality Firewall stop bad records and PII detection mask sensitive fields before anything lands in AWS Glue.
OPEN TABLE FORMAT
AWS Glue: the writer, the catalog, and the Iceberg format
Dataddo delivers your data as standard Apache Iceberg tables. Here is what the managed writer does, how it works with catalogs, and what the Iceberg format gives every downstream engine.
The Dataddo writer
A managed, incremental writer that maintains real Iceberg tables, not raw files.
- Writes in namespace.table form and auto-creates the table on first write
- Write modes: insert (append) and truncate_insert (full replace)
- Manages both the Iceberg metadata and the underlying data files
- Incremental, scheduled loads from 400+ Dataddo sources
- Namespace names cannot contain whitespace
AWS Glue Data Catalog
Dataddo registers Iceberg tables directly in your Glue Data Catalog, backed by S3.
- Tables registered in the Glue database as database.table
- Registered on first write; data stored in the backing S3 location
- Auth: AWS access key, secret and region (IAM: manage Glue, read/write S3)
- Immediately queryable from Amazon Athena
- Also readable by Redshift Spectrum and EMR/Spark
What the Iceberg format gives you
Because the output is standard Apache Iceberg, every downstream engine gets warehouse-grade table semantics on open storage.
- ACID snapshot isolation for consistent reads during writes
- Schema evolution: add, rename, reorder or retype columns with no rewrite
- Hidden partitioning and partition evolution, no manual partition filters
- Time travel and snapshot rollback by ID or timestamp
- Engine-agnostic: Spark, Flink, Trino, Snowflake, Athena, BigQuery, Dremio
WITH VS. WITHOUT
Who carries the load when things change upstream
Keep the same sources flowing into AWS Glue - and see who owns it when an API, schema, or endpoint changes:
| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| API or auth change | You discover the breakage and scramble to fix it. | We update the connector and restore the pipeline - often before you notice. | Files keep landing in AWS Glue |
| Schema drift | Columns change and pipelines break or corrupt data silently. | Detected automatically and handled by configurable rules. | Only clean data lands in AWS Glue |
| Endpoint deprecated | You re-engineer the integration. | We own the update - the data contract holds. | Your AWS Glue loads keep working |
| Missing connector | You build and maintain a custom integration. | We build it and maintain it, under a ~4-week SLA. | Any source can reach AWS Glue |
| Silent degradation | You find out when a report or model run fails. | Proactive monitoring catches anomalies and delays first. | Issues caught before your lake reads bad files |
| Debugging | You dig through logs across disconnected tools. | Run histories, payload inspection, and end-to-end lineage in one place. | Faster root-cause, less downtime |
USE CASES
What data teams build on AWS Glue
A data lake earns its keep when it feeds real work. Here are common ways teams put AWS Glue to use, and the Dataddo connectors that keep each one supplied - all landed in open formats, on your schedule.
Unify marketing and advertising data
Land campaign, spend, and web-analytics data from every channel into AWS Glue for attribution, reporting, and marketing-mix modeling across tools.
Facebook Ads
Google Analytics 4
YouTube Analytics
Offload databases for analytics
Replicate operational databases into AWS Glue so heavy analytical and historical queries run on the lake instead of your production systems.
SQL Server
MongoDB
Feed data science, ML, and AI
Land large, raw datasets as Parquet for feature engineering, model training, and notebook exploration with Spark, pandas, or your ML stack.
Kafka
Archive raw data at low cost
Keep a cheap, long-term history of SaaS, CRM, and finance data in open formats for compliance and audit, without warehouse storage bills.
NetSuite
Stripe
Case Studies
See what data teams build with Dataddo
65,000 Social Media Accounts. One Platform. Zero Manual Authorizations.
Beauty & Consumer Goods
How Livesport Activates Data, Saves Engineering Resources with BigQuery and Dataddo
Entertainment
How ID&T Group Activates Data from 1M+ Festival Fans and Dozens of Social Accounts
Entertainment
How Sensire Accelerated Migration of a Proprietary On-Premise Data Infrastructure to the Cloud with Dataddo
Healthcare
How Ringside.ai Builds a Data Product Better and Faster Using Dataddo
Marketing
How Publicis Groupe Brasil Uses Dataddo's API to Scale a Data Product
Advertising
Pricing
Priced per data flow. Predictable as you grow.
A fixed monthly price based on the number of data flows you run. Start with a free trial, and move to custom enterprise pricing when you need tailored contracts or volume pricing.
FAQ
AWS Glue + Dataddo, answered
What file formats can Dataddo write to AWS Glue?
Parquet, CSV, JSON, and JSONL. Each flow run writes a file in the format you pick, with format-level controls - CSV delimiter, header, and date format, or the timestamp unit for Parquet, JSON, and JSONL.
How does partitioning work?
Each flow run lands as its own dated file, so AWS Glue is organized as a date-partitioned dataset. Query engines then read only the partitions they need instead of scanning everything, which keeps queries fast and costs predictable.
How does Dataddo authenticate to the AWS Glue Data Catalog?
With an AWS access key ID, secret access key and region (no whitespace in any of them). The IAM user or key needs permission to create and update Glue databases and tables and to read and write the S3 location behind them.
How does Dataddo register tables in AWS Glue?
As Apache Iceberg tables in the Glue database you specify, using database.table naming (the database name cannot contain whitespace). Tables are registered on the first write if they do not exist, and data lands in the backing S3 location - queryable from Amazon Athena.
What write modes does Dataddo support for AWS Glue Iceberg tables?
insert (append rows, the default) and truncate_insert (empty the table, then write current data). Iceberg tables only - there are no CSV, JSON or Parquet file options - and Dataddo manages both the Iceberg metadata and the data files.
What Iceberg capabilities do the Glue tables Dataddo writes have?
Because they are standard Apache Iceberg tables, you get ACID snapshot isolation, safe schema evolution (add, rename, reorder or retype columns with no rewrite), hidden partitioning that prunes files automatically, and time travel to query or roll back to an earlier snapshot.
Which engines can read the Glue Iceberg tables besides Athena?
Registration in the Glue Data Catalog makes them readable across the AWS analytics stack (Athena, Redshift Spectrum, EMR/Spark) and by other Iceberg engines such as Trino, Snowflake, Flink and Dremio - the same open table, no copies and no lock-in.
Can Dataddo append or replace files?
Yes. Insert keeps writing new files, and create-new-or-replace overwrites the file at the same name. For lakehouse table formats like Apache Iceberg, Dataddo applies warehouse-style write modes to the table itself.
How is my data secured?
Dataddo is SOC 2 Type II and ISO 27001 certified. Data is encrypted in transit and at rest, PII can be masked or hashed, and EU or US data residency is available. You connect AWS Glue with your own keys or an assumed role.
Will this run up my storage bill?
Incremental loads write only new or changed data, and columnar Parquet compresses well - so you store and scan less. You control the schedule and the partition layout, which keeps storage and query costs predictable.
Am I locked in?
No. Data lands in open file formats in a bucket you own, and pipelines are storage-agnostic - you can add or switch object stores, or point the same sources at a warehouse, without rebuilding anything.