Unlocking Cross-Engine Analytics with Cloudera's Open Lakehouse   Unlocking Cross-Engine Analytics: Why Open Table Formats Are Rewriting the Data Playbook Organizations today are trying to make one strategic move: build a data architecture that keeps them agile without forcing them into a single vendor’s ecosystem. As analytics platforms mature and hybrid cloud becomes the norm, teams want to analyze the same datasets from different engines—without endlessly copying data across environments. This is where the modern lakehouse has begun to shift. Open table formats such as Apache Iceberg, paired with platforms that embrace openness, are making “zero-copy analytics” not just possible, but practical. And when Iceberg tables are managed on Cloudera's Data & AI platform different compute engines  can operate on one authoritative dataset with consistent governance and no movement of data. Why This Matters: The End of the Data Silos Era Enterprises have spent years fighting a losing battle against data fragmentation. Each new tool required a new copy of the data, new pipelines, new governance layers, and new storage costs. The problem wasn’t lack of tools; it was lack of interoperability. Iceberg changes this dynamic by providing a shared, open metadata and table structure. When that format sits inside a fully governed environment , Cloudera’s SDX, running on cloud object storage it becomes a foundation that multiple engines can trust. The result: One dataset. Many engines. No duplication. No lock-in. Compute Independence: The New Strategic Advantage Snowflake, Databricks, and Cloudera each excel at different types of workloads. In the past, choosing one often meant giving up the strengths of the others or paying heavily to replicate data so each engine could run the same workloads. With Iceberg, enterprises no longer think in terms of which platform owns the data. Instead, they think: Which engine is best suited for this job? And all of them read the same Iceberg tables and this is what real compute flexibility looks like. Governance Without Friction Historically, governance has been the tradeoff: the more engines accessing data, the harder it is to enforce consistent controls. Cloudera’s Shared Data Experience (SDX) shifts this conversation. Policies, lineage, metadata, and audit trails are defined once, at the Iceberg table layer, and inherited by external engines as they query the data. This consistency becomes especially critical in regulated industries where governance can’t break when data moves across platforms. The strategic takeaway: Organizations finally get multi-engine agility without losing enterprise-grade governance. A Future-Proof Architecture for Hybrid and Multi-Cloud Most enterprises aren’t planning for a single-cloud future. They’re planning for: Public cloud + private cloud Multiple clouds across business units On-prem workloads where needed Edge analytics where it matters An open Iceberg data layer managed on Cloudera supports all of these deployment patterns. It provides a common foundation while letting each team scale workloads with the engine that suits their needs. Cost Efficiency Through Zero-Copy Design As storage and compute costs grow, eliminating unnecessary data duplication becomes a financial advantage, not just a technical one. A shared Iceberg layer eliminates: Redundant ETL pipelines Copies of the same table in multiple warehouses Storage overhead from duplicating historical data Latency added by moving data across clouds The core value proposition of Cloudera’s Iceberg REST catalog is simple but transformative: your Iceberg tables remain exactly where they were created on Cloudera’s Lakehouse platform, and third-party compute engines like Databricks, Snowflake, or any other Iceberg-compatible engine can query those same tables directly as-is. No data copies and no lock-in. No complex replication pipelines. This is the essence of zero-copy data collaboration. When an organization creates Iceberg tables within Cloudera’s Data & AI platform, those tables are stored in cloud object storage (S3, ADLS, GCS) with metadata managed by Cloudera’s REST catalog. External engines don’t need to import, replicate, or maintain separate copies of this data. Instead, they connect to Cloudera’s REST catalog using standard Iceberg REST APIs and directly access the authoritative tables managed by Cloudera. The data files never leave their original location; only lightweight metadata pointers flow through the REST catalog to coordinate access across engines. This architecture delivers profound strategic benefits: organizations eliminate data duplication across platforms, maintain unified governance through Cloudera’s SDX, avoid vendor lock-in by keeping data in open formats on their own storage, and gain the flexibility to use any compute engine best suited for each workload—all while querying the same authoritative dataset. As Cloudera’s blog on democratizing data for AI articulates, this zero-copy approach fundamentally changes how enterprises collaborate with their data. How Cloudera’s REST Catalog Enables This   REST Catalog as Universal Metadata Broker Cloudera’s REST catalog implements the open Iceberg REST specification, providing a language- and engine-agnostic HTTP/HTTPS API for table metadata operations. Any Iceberg-compatible engine (Databricks, Snowflake, Trino, Flink) can authenticate to this catalog and retrieve table metadata without requiring proprietary connectors or custom integrations. The REST catalog exposes standard endpoints for table discovery, schema retrieval, snapshot inspection, and commit operations. This universal accessibility means that once a table is cataloged in Cloudera, it becomes immediately queryable by any conformant compute engine—no import or translation layer needed. Coordinated Metadata Updates Across Engines When a third-party engine writes to a Cloudera-managed Iceberg table, it doesn’t modify the data files directly or maintain its own metadata store. Instead, it sends a commit request to Cloudera’s REST catalog with the proposed metadata changes (new snapshot ID, updated manifest lists). The REST catalog validates the commit using optimistic concurrency control: if the table’s current snapshot matches the engine’s expected base snapshot, the commit succeeds and the REST catalog atomically updates the authoritative metadata. If another engine has already committed changes, the request is rejected, and the engine must refresh its metadata and retry. This protocol ensures that all engines—whether Cloudera-native or external—always see consistent metadata and that concurrent writes don’t corrupt table state, all without requiring data movement or engine-to-engine coordination. Data Files Remain Stationary The REST catalog stores only metadata—table schemas, snapshot manifests, partition information, and statistical summaries. The actual data files (Parquet, ORC, Avro) remain in their original cloud object storage locations where Cloudera initially wrote them. When Databricks queries a Cloudera Iceberg table, it calls the REST catalog API to retrieve the current snapshot metadata, which includes manifest files listing all relevant data file paths and their column-level statistics (min/max values, row counts, null counts). Databricks then uses its own compute to read those Parquet files directly from object storage—using the same S3, ADLS, or GCS paths that Cloudera uses. At no point does Databricks import the data into its own storage layer or create a proprietary copy. Similarly, if Snowflake queries the same table, it too retrieves metadata from Cloudera’s REST catalog and reads the identical Parquet files. The data files themselves never move; only their locations and statistics flow through the REST catalog as lightweight metadata. Efficient Query Planning Through REST Catalog Metadata The REST catalog’s metadata API allows external engines to perform intelligent query planning without scanning actual data. When Snowflake issues a query against a Cloudera Iceberg table, it first retrieves the table’s manifest files from the REST catalog. These manifests contain detailed file-level and column level statistics: which data files exist, their partition values, min/max bounds for each column, row counts, and null counts. Snowflake can use this metadata to prune entire files that don’t match the query predicates (e.g., if querying transactions from Q4 2024, it skips all files with partition dates outside that range). Iceberg’s hidden partitioning means partition logic is encoded in metadata, not in directory paths, so any engine can leverage it. This metadata-driven pruning happens before any data file is touched, drastically reducing I/O and costs. Because all engines retrieve this same metadata from Cloudera’s REST catalog, they all benefit equally from optimizations like file-level pruning and schema evolution causing no engine to have a privileged position or require custom metadata syncing. Companies adopting this approach will see  meaningful reductions in storage footprint and pipeline maintenance. Cloudera’s Lakehouse Optimizer further enhances this by automatically compacting files, tuning metadata, and reducing query overhead. What This Shift Signals for the Industry  The rise of Iceberg as the de facto open table standard and its adoption by Cloudera, Snowflake, and Databricks signal a broader movement: Analytics platforms will differentiate on compute, not data formats. Data governance will centralize, not splinter. Open standards will matter more than closed ecosystems. For enterprise leaders, this is a chance to reset architectural strategy around openness, portability, and long-term efficiency. Summary A data architecture built on Iceberg tables managed by Cloudera  unlocks something the industry has been pushing toward for years: true cross-engine analytics with unified governance and zero data movement. At this juncture different  compute engines just become complementary rather than competing destinations. The payoff is strategic: A single, trusted data layer Freedom to use any compute engine Lower storage and pipeline cost Stronger governance and compliance Faster path from data to insight This isn’t just a technical pattern shift  - it’s a blueprint for how modern enterprises will run analytics moving forward ! ... View more