Zero ETL, Real-Time Enterprise: Direct Data Sharing That Simplifies Analytics, AI, and Compliance
For decades, organizations have moved data from where it’s created to where it’s analyzed through extract-transform-load (ETL) pipelines. That model introduced delay, duplicated data, and accumulated governance risk. “Zero ETL” turns this on its head by making data available for analytics, AI, and compliance directly—often without copying—so consumers can query, join, and act on fresh data in place. The result is a real-time enterprise: decisions triggered by events within seconds, fewer brittle pipelines to maintain, and governance built into the sharing fabric rather than bolted on later.
This shift is not marketing spin. It is a convergence of mature capabilities: zero-copy sharing in cloud data platforms, change data capture (CDC) streams from operational databases, open table formats on data lakes, data clean rooms for privacy-preserving collaboration, and semantic layers that push transformation to read-time rather than pre-materialization. The implications reach across technology and business: faster experimentation, fewer copies, lower latency, and stronger control.
What “Zero ETL” Really Means
Zero ETL does not imply zero transformation or zero modeling. It means removing the routine, batch-oriented copying and reformatting that used to be mandatory between every system. Instead, publishers expose governed, query-ready datasets or event streams; consumers connect directly to those artifacts with shared semantics and access controls. Transformations become:
- Lightweight, in-engine semantic logic (views, policies, metrics definitions, user-defined functions) rather than precomputed copies.
- Streaming enrichment and materialized views that update continuously rather than nightly jobs.
- Contracted interfaces that evolve safely without breaking downstream consumers.
In practice, zero ETL is the combination of zero-copy sharing, stream-first data movement, and transform-on-read semantics wrapped in strong governance. It reduces redundancy and latency while maintaining consistency.
Why Real-Time Enterprises Need It
Competition compresses decision windows. Pricing needs to adapt to demand spikes in minutes, not days. Fraud must be stopped before authorization, not reconciled after settlement. Customer experiences depend on context that is only relevant for seconds. Traditional ETL introduces delays, brittle dependencies, and blind spots that sabotage these goals.
Consider three common triggers for change:
- Latency pressure: Streaming purchases, sensor alerts, or support interactions can drive cross-sell offers, dispatches, or triage workflows immediately—if the data is queryable where needed.
- Compliance pressure: Regulations demand provable control over data use and copies. Direct sharing reduces uncontrolled proliferation of sensitive data.
- Cost pressure: Each pipeline and copy adds storage, compute, and maintenance costs. Sharing reduces blast radius and spend while improving agility.
Core Architectural Principles
Event-first, immutable logs
Capture changes as append-only events (orders, telemetry, profile updates). Immutable logs are the system of record, supporting both real-time processing and reproducible analytics. Downstream systems materialize their own views from the log.
Data products with contracts
Each domain publishes a documented, versioned dataset or stream backed by an explicit contract: schema, semantics, SLOs for freshness and availability, and access policies. Producers own quality and evolution; consumers rely on stability.
Transform on read, materialize when necessary
Push transformations into query engines via views, policies, and metrics layers. Only materialize when latency or cost demand it (e.g., operational dashboards or low-latency features).
Least-privilege, policy-driven access
Apply row- and column-level security, masking, and purpose-based access centrally. Avoid copying data to enforce policies—attach policies to shared objects.
Direct Data Sharing Patterns
Zero-copy sharing in cloud data platforms
Major platforms provide native sharing that grants governed access without duplicating data:
- Snowflake Secure Data Sharing: share tables, views, and functions across accounts and clouds without copying. Data providers control revocation and see usage metrics.
- Databricks Delta Sharing: an open protocol to share Parquet/Delta tables across platforms via short-lived tokens, with fine-grained access control and auditability.
- BigQuery Analytics Hub: curated exchanges where datasets are subscribed to and queried directly, often with data masking and row-level filtering.
These approaches collapse integration cycles. A partner can join your live order table to their marketing events within minutes, with your policies enforced at query time.
Open table formats and catalog federation
Apache Iceberg, Delta Lake, and Apache Hudi bring ACID transactions and versioned metadata to data lakes. Paired with a unified catalog (e.g., Glue, Unity Catalog), they let multiple engines (Spark, Trino, Flink) query the same table concurrently. Share the table; avoid copies. Time-travel features enable reproducible analytics without backfilling pipelines.
Change data capture (CDC)
CDC tools (Debezium, native MySQL/Postgres logical replication, SQL Server CDC) stream inserts, updates, and deletes from operational databases into a bus (Kafka, Pulsar) or directly into lakehouse tables. Consumers materialize state via stream-to-table sinks or subscribe to change streams for reactive workflows. This minimizes ETL by replicating exactly what changed, not entire tables.
Event streaming and stream tables
Modern stream processors (Flink, Spark Structured Streaming, ksqlDB) and low-latency OLAP stores (Apache Pinot, ClickHouse, Druid) maintain continuously updated materialized views. Many support “streaming tables” that look like SQL tables but are fed by event topics. Business users query fresh aggregates without intermediate batch jobs.
Data virtualization and federation
Engines such as Trino/Presto, Dremio, and BigQuery external tables can federate queries across multiple data sources. Views knit together operational stores, S3/GCS, and partner shares. This pattern avoids copying but requires careful performance engineering (pushdown, caching) and policy enforcement.
API-first and graph access
Not every consumer needs SQL. Some prefer APIs with strong contracts. GraphQL and gRPC gateways backed by shared tables or streams deliver low-latency, purpose-limited data slices with built-in access control and caching. A well-designed API can be the zero-ETL interface for operational use cases.
Data clean rooms
Clean rooms allow multiple parties to compute joint insights without exposing raw data. Providers bring first-party data; partners bring their datasets; the room runs vetted queries and returns aggregated results. This fits zero ETL by preventing copies while enabling collaboration, and it simplifies compliance for advertising, retail, and healthcare use cases.
Real-World Scenarios
Fintech partner integrations
A digital bank shares a secure view of transaction metadata (masked PAN, merchant category, amount, timestamp) with a fraud analytics partner via Delta Sharing. The partner joins it with device risk scores and returns per-transaction risk probabilities within seconds. The bank never ships raw PII; access is revocable, and lineage is audited. The result: card-present fraud dropped 18% with no new batch pipelines.
Retail merchandising and supply chain
A retailer’s replenishment engine reads store-level sales and inventory deltas from a CDC stream. A shared streaming table in Pinot exposes near-real-time “units on hand” and “sell-through” metrics to planners and vendors. Vendors see only their SKUs and stores via row filters. Stockouts fall because planners see anomalies within minutes, not after nightly ETL completes.
Healthcare coordination
A hospital network uses a clean room to collaborate with a pharmaceutical company. De-identified patient cohorts are defined with privacy thresholds and approved queries. The pharma team analyzes outcomes and adherence without taking custody of raw PHI. Because sharing is direct and policies are embedded, the review board’s approval time shrinks from months to weeks.
Manufacturing IoT and predictive maintenance
Factory sensors stream telemetry to Kafka; Flink aggregates by machine and writes to an Iceberg table with 1-minute snapshots. Engineers and dashboards query the same table for trend analysis, while an ML feature pipeline consumes the stream for online scoring. No export jobs; no lag. A bearing failure model triggers a work order within two minutes of anomaly detection.
Media and advertising attribution
An ad network provides publishers with near-real-time campaign performance via secure data shares. Publishers combine it with their first-party engagement data in a clean room, running vetted attribution models. Privacy is preserved, latency is low, and both parties avoid duplicative ETL.
Analytics Without ETL Friction
Semantic layers and metrics governance
A semantic layer (dbt Semantic Layer, Cube, AtScale, or a platform-native metrics store) defines business concepts—active users, churn, gross margin—once. BI tools, notebooks, and APIs query via this layer, which compiles to underlying engines. The layer enforces consistency and reduces physical copies of “metrics tables.” It also enables transform-on-read with caching where appropriate.
Streaming OLAP for sub-second insights
Systems like Pinot and ClickHouse ingest streams and serve low-latency aggregates. With real-time upserts and rollups, analysts explore fresh data without staging ETL. For example, a support operations team can query “tickets created in the last 10 minutes by region and severity” with dashboard latencies under 500 ms, driven directly from the stream.
Interoperability with BI and notebooks
Direct shares appear as native tables to BI connectors (JDBC/ODBC) and Python clients. Catalogs expose lineage and tags so analysts understand provenance. Cached result sets and incremental models keep costs predictable while avoiding duplication.
AI and ML in a Zero-ETL World
Unified feature flows
Feature stores (Feast, Tecton, platform-native stores) bridge offline and online views. They read from shared tables or streams, compute features with streaming pipelines, and serve them with point-in-time correctness. Because features source from the same shared artifacts used by analysts, training-serving skew shrinks, and governance is consistent.
Streaming vector pipelines
LLM applications often rely on retrieval augmented generation (RAG). In zero-ETL mode, document and event updates flow into an embedding service; vectors are upserted into a store (pgvector, Pinecone, Weaviate, OpenSearch) within seconds. A shared change stream ensures every consumer sees the same updated corpus without copies. Row- and column-level policies guard sensitive snippets; masking is applied before embedding to avoid leaking secrets.
Operational data as context windows
Customer service copilots can query shared operational tables for the latest orders or entitlements at inference time. Because access is direct and scoped, the model sees only what its role permits. Fall back to canned responses when freshness SLOs aren’t met; log all prompts and retrieved context for audit and improvement.
MLOps and lineage
Training datasets are referenced by table versions (time travel) rather than exported files. Model cards record exact table versions and feature definitions. When a producer changes a contract, downstream model retraining triggers automatically. This reduces surprise drift and supports reproducibility for audits.
Governance and Compliance by Design
Data contracts and schema governance
Contracts live in version control and a catalog. They define schemas, allowed operations, and deprecation windows. Producers use schema registries (Avro, Protobuf, JSON Schema) with compatibility rules; consumers subscribe to notifications. Changes roll out with additive fields first, then guarded deprecations.
Row/column-level security and dynamic masking
Policies enforced by the engine limit exposure without copies. Examples:
- Row filters by tenant, region, or business unit using attributes in the user’s identity token.
- Column masking for PII (hashing, partial reveal of PAN or SSN) and dynamic redaction for unapproved purposes.
- Purpose-based access policies that check the “purpose” claim (e.g., “fraud detection”) to permit sensitive joins only when compliant.
Consent, purpose limitation, and OPA
Consent states are modeled as tables and joined into policy decisions. Open Policy Agent (OPA) or platform-native policy engines evaluate requests: who, what, why. The same policy logic governs SQL queries, APIs, and ML training jobs for consistency.
Privacy-enhancing technologies
Tokenization and format-preserving encryption protect identifiers while preserving joinability where appropriate. Clean rooms restrict computations to audited templates. Differential privacy adds noise to aggregated outputs when sharing across parties. Where feasible, confidential computing (hardware enclaves) can protect data-in-use for highly sensitive workloads.
Right to be forgotten in streaming systems
Implement erasure handling with tombstone events and compaction in logs, plus key erasure in downstream state stores. Iceberg/Delta provide delete semantics with metadata updates; a background process rewrites files to remove deleted rows. Maintain a retention index to propagate deletions automatically across materializations.
Auditability and lineage
Systematically capture lineage (OpenLineage/Marquez, DataHub, Amundsen, Collibra). Log who queried what, when, and for what purpose. Tie lineage to contracts and releases so auditors can reconstruct what data fed a report or model at a given time.
Reliability, SLAs, and Observability
Freshness and availability SLOs
Publish explicit SLOs: e.g., “P95 freshness under 60 seconds; availability 99.9% during business hours.” Expose metrics (lag, throughput, error rates) via dashboards and into Service Level Objectives with alerting. Consumers build fallbacks if SLOs aren’t met.
Backpressure, idempotence, and replay
Design stream processors with backpressure handling, exactly-once or effective-once semantics, and idempotent sinks. Keep retention long enough to reprocess from checkpoints. For tables, use merge-on-read and upsert operations keyed by event IDs to avoid duplicates.
Data quality and observability
Embed automated checks (Great Expectations, Soda, Deequ) at production boundaries: schema conformity, null rates, value ranges, referential integrity, distribution drift. Alert on anomalies and route to owning domain teams. Observability tools measure freshness, lineage completeness, and test coverage.
Disaster recovery and multi-region
Replicate logs and tables across regions with synchronous or near-synchronous replication depending on RPO/RTO targets. Ensure catalogs and policies replicate too. Regularly test failover for shares and stream readers to confirm consumers reconnect automatically.
Performance and Cost Engineering
Pushdown and compute locality
Choose engines that push filters, projections, and aggregations down to storage layers. Co-locate compute with data to avoid egress. Prefer vectorized scans, columnar formats (Parquet), and compressed dictionary encoding to minimize IO.
Hot/cold tiering and materialized views
Keep the freshest minutes or hours in fast storage (SSD-backed) and older data in cheaper object stores with cache warming. Materialize common aggregates with refresh policies; invalidate intelligently on updates. For ad hoc exploration, use result caching with TTLs aligned to freshness SLOs.
FinOps for shared data
Tag shared resources for cost attribution. Rate-limit or quota heavy queries. Offer serverless endpoints for spiky workloads and provisioned clusters for predictable loads. Beware cross-cloud and cross-region egress fees in partner sharing; prefer private links and exchange-native sharing.
Query governance and admission control
Establish guardrails: time limits, row caps, and kill switches for runaway queries. Provide guidance and templates for efficient joins and partition pruning. Use workload isolation so exploratory queries don’t starve production flows.
Security and Zero Trust
Identity federation and fine-grained authorization
Integrate with enterprise identity providers (OIDC/SAML). Use short-lived tokens with attributes that drive row/column policies. Service accounts get narrowly scoped roles; rotate credentials automatically.
Encryption and key management
Encrypt data at rest and in transit with managed keys (KMS/HSM). Employ client-side encryption or envelope encryption for highly sensitive fields. For multi-party computation, consider enclave-backed workflows (e.g., Nitro Enclaves, SGX) where applicable.
Network posture
Prefer private connectivity (PrivateLink, VPC peering) for shares and stream endpoints. Segment by environment (dev/test/prod) and context (internal vs. partner). Monitor egress and restrict public endpoints by default.
Vendor Choices and Interoperability
Open formats as an insurance policy
Store canonical data in open formats (Parquet, Iceberg/Delta metadata) so multiple engines can read it. Even when you use a native sharing feature, retain an open-format path for portability and recovery.
Lock-in trade-offs and contracts
Native platform features often deliver the best latency and simplicity. Balance this with contract tests and exit strategies: prove you can replay CDC to an open table or expose shares via an open protocol. Keep business logic in portable layers (SQL, dbt models, semantic definitions) where feasible.
Implementation Roadmap
1. Assess and prioritize value streams
Map where latency hurts: fraud, pricing, inventory, customer experience. Quantify potential impact (e.g., “Reduce stockouts by 30%, incremental revenue +2%”). Identify regulatory hotspots where copies and shadow pipelines create risk.
2. Define the data product catalog
For each domain, list candidate products (tables, streams, features) with owners, SLOs, and sensitivity. Start with slices that unlock cross-domain value: orders, inventory deltas, customer profiles, telemetry aggregates.
3. Choose the sharing backbone
Decide on the primary modes: native zero-copy sharing in your data platform, Delta/Iceberg sharing for lakehouse, CDC + Kafka for changes, clean room for external collaborations. Integrate a unified catalog with policy enforcement.
4. Build a reference architecture
Stand up a pilot with end-to-end flow: operational DB → CDC → stream → lakehouse table → shared view with row/column policies → BI and ML consumers. Include observability, lineage, and quality checks. Prove rollback and revocation.
5. Operationalize contracts and CI/CD
Put schemas and policies in version control. Add CI to validate compatibility and run data tests on sample payloads. Automate catalog registration and policy updates on deploy. Fail builds on breaking changes unless override is approved.
6. Establish SLOs, governance, and runbooks
Publish freshness/availability SLOs. Create runbooks for lag spikes, schema breaks, and consumer impact. Implement incident routing to domain owners. Include cost SLOs (budget caps) and alert when workloads drift.
7. Scale out with platform capabilities
Offer self-service templates: create a data product, publish a share, add a policy, register a quality test, and expose a metric. Provide golden patterns for CDC, stream processing, and materialized view refresh. Centralize security and observability while federating data ownership.
Common Pitfalls and How to Avoid Them
“Zero ETL” as a license for chaos
Skirting modeling and governance leads to semantic drift. Solve with a semantic layer, data contracts, and product ownership. “Zero ETL” is not “no discipline.”
Over-federation without guardrails
Unbounded federation can be slow and expensive. Push heavy joins into engines that co-locate compute and storage; cache wisely; monitor query plans and add pushdown connectors or materialized views.
Tight coupling to operational databases
Running heavy analytics on OLTP systems degrades performance. Use CDC to offload, then share from analytical stores built for concurrency. Where HTAP (hybrid transactional/analytical processing) is appropriate, choose engines designed for it.
Hidden copies for convenience
Teams may keep private exports “just in case.” Enforce policy-based access and provide fast, governed sandboxes with time-bounded, masked snapshots. Track copies in the catalog and alert on drift.
Schema changes without communication
Breaking changes shatter trust. Require compatibility checks and deprecation windows. Publish migration guides and sample payloads. Automate version negotiation in streams and APIs.
Ignoring purpose and consent
Purpose limitation must be enforced, not assumed. Encode consent and purpose into access policies; block disallowed joins. Maintain auditable logs of purpose claims.
Measuring Success
- Latency: median and P95 data freshness from event creation to consumer query.
- Copy reduction: number of authoritative copies per sensitive dataset, and total storage footprint.
- Pipeline maintenance: hours spent maintaining batch jobs versus streaming and semantic definitions.
- Query success and SLO adherence: percentage of queries meeting freshness/availability targets.
- Incident rate: data quality or schema break incidents per quarter.
- Compliance posture: audit findings closed, purpose violations prevented, time-to-approve data sharing requests.
- Business outcomes: fraud loss reduction, revenue uplift from real-time personalization, stockout reduction, SLA improvements.
Design Patterns You Can Reuse
CDC-to-lakehouse-to-share
- Capture changes from OLTP via Debezium or native replication.
- Stream into an object store-backed table (Iceberg/Delta) with partitioning by date/time and business keys.
- Publish a governed view with policies and a semantic layer of metrics.
- Share directly to internal domains and external partners with zero-copy.
Streaming features with point-in-time correctness
- Ingest event streams for actions and entities.
- Compute features in Flink with keyed state, writing to an online store.
- Backfill and validate offline features from the same shared table versions.
- Serve features to models; log feature vectors with timestamps for reproducibility.
Privacy-preserving partner analytics
- Tokenize identifiers and map via privacy-safe joins.
- Expose only permitted fields via clean room query templates.
- Apply differential privacy for small cohorts; enforce k-anonymity thresholds.
- Audit all queries and outputs; revoke access on policy changes.
Technology Landscape Snapshot
- Sharing and catalogs: Snowflake Sharing, BigQuery Analytics Hub, Databricks Delta Sharing, Apache Iceberg REST Catalog, Unity Catalog, AWS Glue Catalog.
- Streams and CDC: Apache Kafka, Redpanda, Pulsar, Debezium, native Postgres/MySQL logical replication, SQL Server CDC, cloud-native change streams.
- Processing: Apache Flink, Spark Structured Streaming, ksqlDB, Materialize for streaming SQL.
- OLAP/low-latency: Apache Pinot, ClickHouse, Druid, SingleStore, Rockset.
- Lakehouse: Delta Lake, Apache Iceberg, Apache Hudi.
- Virtualization: Trino/Presto, Dremio.
- Semantic and metrics: dbt, dbt Semantic Layer, Cube, AtScale, MetricFlow.
- Quality and lineage: Great Expectations, Soda, Deequ, OpenLineage/Marquez, DataHub, Collibra, Alation.
- Feature stores: Feast, Tecton, platform-native stores.
- Vector stores: pgvector, Pinecone, Weaviate, OpenSearch, Vespa.
- Policy and security: OPA, platform-native row/column policies, dynamic masking, clean rooms, KMS/HSM, confidential computing.
Organizational Operating Model
Platform team and domain ownership
A central data platform team offers shared capabilities: catalogs, sharing, streaming, security, and observability. Domain teams own data products and quality. This balances standardization with domain expertise.
Product mindset and SLAs
Treat datasets and streams as products with roadmaps, documentation, SLOs, and support channels. Measure adoption and satisfaction. Budget platform cost to domains based on usage with transparent chargeback.
Education and enablement
Train teams on contracts, schemas, and semantic layers. Provide golden path templates and reference implementations. Celebrate reductions in copies and latency as shared wins.
From Batch Age to Event Age: A Cultural Shift
Zero ETL is ultimately a cultural change: design for sharing by default, model events explicitly, publish contracts, and push control into the platform. Teams stop hoarding data or cloning it “just in case.” Instead, they trust the platform to deliver fast, governed access, and they trust domain owners to evolve products responsibly. When that trust is reinforced by observable SLOs, audited policies, and proven reliability, the enterprise becomes genuinely real-time.