If you’ve ever built or maintained a data platform, you know the tradeoffs. Architectures get messy fast. Costs creep higher with every new pipeline. Retaining historical data feels like a constant battle. And once you’re locked into a single vendor’s ecosystem, flexibility goes out the window.
These aren’t abstract problems, but the daily realities that slow down data engineers and architects. That’s why more teams are turning to modern data lakes as a foundation for their platforms. By decoupling storage from compute and embracing open table formats, data lakes create a simpler, more flexible, and more cost-efficient architecture.
With the Fivetran Managed Data Lake Service, you can go even further: automate ingestion, absorb compute-heavy pipeline costs, and free your team from maintaining brittle infrastructure. In this post, we’ll look at the 4 most common challenges technical teams face — and how a managed data lake helps solve them.
1. Complexities causing compliance headaches
Legacy architectures often look like a patchwork of pipelines, warehouses, and bespoke integrations. Data is copied into multiple destinations across multiple clouds (i.e., a many-to-many relationship), creating redundant datasets, brittle pipelines, and compliance risks when it’s unclear where sensitive data lives or which version is definitive.
The solution to this complexity is a universal storage layer with a single integration path. With Fivetran Managed Data Lake, data is ingested once into a centralized, deduplicated store. From there, any downstream service can query it — Databricks, Snowflake, GCP, or whatever your stack requires.
Engineers maintain fewer pipelines, simplify audits, and scale without multiplying complexity.
2. Limited optionality and vendor lock-in risk
Traditional warehouses force you into a single compute engine, tightly coupling storage and compute. Every team — whether they’re building ML models or running BI dashboards — is stuck with the same performance tradeoffs, even though different compute engines are optimized for different kinds of use cases.
The solution is a modern data lake that decouples storage from compute, giving every team the freedom to pick the right tool for the job. Fivetran Managed Data Lake supports open table formats and catalogs, so you can run the best query engine for each workload without rewriting pipelines. Engineers regain flexibility: Databricks for ML, Snowflake for BI, PySpark for batch jobs — all without vendor lock-in.
3. Rising compute costs
Ingesting and re-ingesting data across multiple platforms can drive costs through the roof. Data duplication means organizations often pay for the same workloads multiple times. In many cases, ingestion alone accounts for 20–30% of warehouse compute costs.
Fivetran Managed Data Lake addresses this in 2 ways: Data lands once in the lake, eliminating redundant ingestion. Fivetran also absorbs ingestion costs (which account for 20-30% of compute costs in a data warehouse) outright. That’s not a discount — it’s a fundamental architectural shift that removes one of the biggest drivers of total cost of ownership.
4. Long-term storage of historical data
Historical data is critical for audits, long-term analytics, and recovery, but most warehouses cap retention. BigQuery, for example, only supports 7 days of “time travel.” Teams end up stitching together workarounds or paying for expensive cold storage.
A data lake provides unlimited, cost-effective cold storage and lets you pair it with any query engine. This makes it possible to retain full historical context, build audit trails, and run advanced time-travel queries without hitting limits. With Fivetran Managed Data Lake Service, data teams can preserve history without trading off cost or performance.
Why Fivetran Managed Data Lakes Service
With open table formats, automated integration, and ingestion costs covered, Fivetran Managed Data Lake Service combines the scalability of lakes with the usability of warehouses. For data teams, this means:
- Fewer pipelines to manage and debug
- Freedom to choose best-in-class query engines
- Lower and more predictable total cost of ownership
- Unlimited, flexible data retention
Instead of spending cycles managing brittle pipelines, engineers can focus on building models, applications, and insights that drive the business forward.
If you’re building a future-proof data architecture, a managed data lake removes the hardest engineering roadblocks — while giving you more control, flexibility, and efficiency.
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