CONNECTING TO YOUR REALITY
Integrations that respect how your data estate actually looks
DataCyclic is designed to sit comfortably inside existing data, ETL and archiving landscapes — not replace everything you already have. The goal is simple: plug into your sources and targets with as little friction as possible, while enforcing strong governance along the way.
How DataCyclic thinks about integrations
Most enterprises already have ETL platforms, messaging buses, data warehouses and backup tools in place. A practical archiving platform integrates with that ecosystem instead of forcing a complete rewrite. DataCyclic treats integrations as first‑class: stable, observable entry and exit points that are easy for other tools to call.
Some vendors treat integrations as a long tail of connectors bolted on after the fact. DataCyclic focuses on a different dimension: well‑behaved interfaces (APIs, JDBC, files, object storage) that make it easy to combine our archive flows with your existing orchestration and governance stack.
Whether you orchestrate with Airflow, a commercial ETL tool, or cloud‑native jobs, the integration model is the same: clear inputs and outputs, strong metadata, and predictable behavior under load.
Integration principles
- • No lock‑in: use open formats (Parquet, JSON, CSV) and standard interfaces wherever possible.
- • Observability first: logs and metrics designed to plug into existing monitoring stacks.
- • Security aligned: reuse your identity, network and key‑management patterns.
- • Layered integrations: connect directly where it makes sense, or through your existing ETL / ESB where that is the standard.
Connecting to what you already run
Integrations typically start from two questions: where does the data come from, and where should the long‑term archive live? DataCyclic supports both direct connectivity and “meet‑in‑the‑middle” patterns with your existing ETL and integration tools.
Typical sources
- • Relational databases (Oracle, SQL Server, PostgreSQL, MySQL and others) via JDBC / drivers.
- • Application exports delivered as CSV, JSON, XML, or Parquet files.
- • Existing ETL output folders and landing zones in object storage or file shares.
- • Event and log pipelines that batch older segments for archiving.
In many estates, DataCyclic is simply another well‑behaved consumer of data your ETL or integration platform is already producing.
Typical targets
- • Cloud object storage (S3, Azure Blob, GCS) across hot, cool and archive tiers.[web:92][web:94]
- • On‑prem object stores or HDFS‑compatible systems where cloud is not an option.[web:90][web:94]
- • Downstream analytical systems that read Parquet (lakehouses, warehouses, query engines).
- • Secondary relational stores when you need SQL access to subsets of the archive.
Targets are chosen to balance retrieval speed, cost, and regulatory requirements such as WORM / retention‑lock and residency.[web:92][web:94]
Designed to coexist with your ETL and integration stack
ETL and data‑movement vendors position themselves around “connect everything to everything”. That layer remains valuable. DataCyclic focuses on a different but complementary problem: giving archived data a durable home with the right structure, policies and access paths.
Ingestion via your ETL
Use existing ETL jobs to land data into a staging bucket or database schema, and let DataCyclic pick it up, optimize it into Parquet, and apply retention rules. Your integration tool stays the “movement” layer; DataCyclic is the archive brain.
Direct connectors where needed
For high‑volume or latency‑sensitive sources, DataCyclic can connect directly (e.g., JDBC or bulk unload) to reduce hops and keep ingestion predictable, while still exposing logs and metrics back into your monitoring stack.
APIs for governance & access
APIs and service interfaces make it easy for catalog, governance, and ticketing systems to look up archive status, trigger legal holds, or request restores without manual coordination.
Typical integration journey
Integrations are usually incremental, not “big bang”. A simple, well‑scoped path keeps risk low and shows value quickly.
Step 1
Integrations workshop
Map core systems, data flows, ETL tools and compliance drivers.
Step 2
Small, real POC
Wire one or two sources into DataCyclic using your existing jobs or a direct connector.
Step 3
Harden the interfaces
Add monitoring, access controls, and runbooks once the pattern is proven.
Step 4
Scale out
Roll out the same integration approach to additional apps, regions and business units.
Want to discuss how DataCyclic fits into your integration landscape?
Share your current ETL and data‑movement stack, key source systems, and compliance pressures. We can walk through practical integration patterns and a low‑risk POC plan tailored to your estate.
Talk about integrations