Most organizations don’t struggle with a lack of data—they struggle with data living in different places, using different standards, and speaking different “languages.” SAP runs critical processes, Qlik powers associative exploration for many business users, and Power BI often becomes the default for reporting and executive dashboards.

The real challenge is getting them to work together cleanly—without fragile manual exports, duplicated logic, or security gaps.

This guide breaks down how custom extensions and custom connectors help you unify Qlik, Power BI, and SAP into a single, reliable analytics ecosystem—plus practical architecture patterns, governance tips, and implementation examples you can apply immediately.

Why “One Ecosystem” Matters (Even When Tools Differ)

Running multiple analytics platforms isn’t inherently a problem. It becomes a problem when:

• Definitions diverge (Revenue in Qlik ≠ Revenue in Power BI)
• Refresh patterns conflict (near-real-time needs vs. daily batch)
• Security and access rules drift
• Teams duplicate the same transformation logic
• SAP data extraction becomes a bottleneck

A unified ecosystem doesn’t mean “one tool.” It means one set of trusted data products, consistent metrics, and shared governance—served to users in the tools they prefer.

What Are Custom Extensions vs. Custom Connectors?

Custom connectors (data access layer)

A custom connector is how a tool reliably connects to a data source or service—especially when the standard connector is missing, limited, or doesn’t meet security/performance needs.

Examples:

• A Power BI custom connector for a proprietary REST API
• A Qlik connector that handles SAP authentication flows securely
• A connector that enforces row-level security rules upstream

Custom extensions (UI + interaction layer)

An extension enhances what users can do inside the BI tool—custom visuals, workflows, write-back actions, guided navigation, embedded apps, or specialized filtering behavior.

Examples:

• A Qlik extension that triggers a workflow (e.g., approve exceptions)
• A Power BI custom visual for domain-specific KPIs
• Embedded SAP operational drill-through from analytics dashboards

In short: connectors move data and enforce access patterns; extensions shape the user experience and workflows.

Where SAP Fits: Operational Truth vs. Analytics Performance

SAP systems (ECC, S/4HANA, BW, etc.) are often the system of record. But using SAP directly for broad analytics can be expensive and limiting because:

• Transactional systems aren’t optimized for high-concurrency analytics
• Queries can impact operational performance
• Data models can be complex and inconsistent across modules

A common best practice is to treat SAP as the authoritative source, then publish analytics-ready datasets into a warehouse/lakehouse layer. If you’re building pipelines for higher-frequency needs, it helps to follow proven integration patterns—especially for transactional sources (see: Real-time reporting with BigQuery: how to integrate transactional systems the right way).

Architecture Patterns That Actually Work

Pattern 1: “Shared Semantic Layer” (Recommended for consistent KPIs)

Goal: Qlik and Power BI consume the same curated models and metric definitions.

How it works:

• SAP data is extracted to a centralized platform (warehouse/lakehouse)
• Transformations are standardized (e.g., dbt models)
• Qlik and Power BI connect to the curated datasets

Why it’s effective:

• One source of truth for metrics
• Less duplicated logic
• Easier governance and auditing

If you’re standardizing transformations, it’s worth implementing a disciplined modeling approach (see: Data modeling and transformation with dbt: a practical end-to-end guide).

Pattern 2: “Tool-Specific Semantic Layers” (Faster to start, harder to scale)

Goal: Move quickly by allowing each tool to define its own logic.

How it works:

• SAP → staging/warehouse
• Qlik script defines business rules
• Power BI datasets define separate business rules

Trade-off:

• Faster initial delivery
• Higher long-term cost (KPIs drift, governance pain, duplicated work)

This pattern often becomes the “why don’t numbers match?” problem.

Pattern 3: “Operational + Analytical Split” (Best for hybrid use cases)

Goal: Combine operational drill-down (SAP) with analytical exploration (Qlik/Power BI).

How it works:

• Analytics dashboards show trends and KPIs from curated data
• Drill-through links send users into SAP (or SAP Fiori apps) for transactions
• Extensions provide guided navigation and context

When it shines:

• Finance and procurement workflows
• Supply chain exceptions and approvals
• Customer service analytics with operational next steps

When You Should Build a Custom Connector (and When You Shouldn’t)

Build a custom connector when:

• You need a non-standard authentication flow (SSO, OAuth variations, JWT, mutual TLS)
• You must enforce enterprise governance (auditing, data entitlements, row-level security)
• Performance requires query pushdown, pagination, incremental sync, or caching
• Standard connectors fail for SAP-specific complexity (BAPIs/ODP nuances, gateway constraints)

Don’t build one when:

• A mature connector already exists and meets requirements
• You can solve it with a stable integration layer (ELT/ETL) and connect to the warehouse
• Your real problem is data modeling (not connectivity)

For many teams, a modern ELT approach reduces the need for custom connectors by standardizing ingestion and transformations. If you’re designing dependable pipelines, this is a strong reference point: Airbyte made practical: how to build reliable data integrations and ELT pipelines.

Practical Examples: What “Custom” Looks Like in the Real World

Example 1: A unified “Customer 360” across SAP + Qlik + Power BI

Problem: Sales uses Power BI, operations uses Qlik, and customer master data lives in SAP.

Solution:

• Extract SAP customer, billing, and order data to the warehouse
• Model conformed dimensions (Customer, Product, Region)
• Publish curated marts for:
• Power BI executive reporting
• Qlik exploratory analysis (associative discovery)
• Add drill-through links to SAP for transaction-level actions

Result: One customer definition, two consumption styles, fewer reconciliation meetings.

Example 2: Qlik extension for exception workflows (“Approve, comment, assign”)

Problem: Users see anomalies but can’t act without leaving dashboards.

Solution:

• Build a Qlik extension that:
• Writes back comments/approval status to a workflow table
• Triggers a notification (e.g., ticket or email)
• Logs actions for auditability

Result: Analytics becomes operational—without turning your BI tool into a full application.

Example 3: Power BI custom connector for a governed API layer

Problem: Direct database access is restricted; all analytics must go through a governed API.

Solution:

• Implement a Power BI custom connector that:
• Handles OAuth/SSO
• Supports incremental refresh patterns
• Enforces user entitlements via API claims
• Standardizes pagination and error handling

Result: Centralized access control and audit trails, with a consistent developer experience.

Security, Governance, and Compliance: The Non-Negotiables

When you connect SAP, Qlik, and Power BI, security can’t be an afterthought. Focus on:

1) Identity and access management

• Centralize authentication (SSO where possible)
• Map identities consistently across tools
• Avoid embedding credentials in scripts or desktop files

2) Row-level and object-level security

• Decide where security lives:
• In the warehouse (preferred for consistency), or
• In each tool (risk of drift)
• Document security rules as reusable policies

3) Auditing and lineage

• Track who accessed what, when, and through which tool
• Maintain lineage from SAP source tables through transformations to dashboards

4) Version control for “analytics code”

• Extensions, connectors, and transformations should be treated like software:
• Git versioning
• CI checks
• Peer reviews
• Release process

Performance Tips for a Smooth User Experience

Even the best integration fails if dashboards are slow or unreliable. Practical ways to improve performance:

• Push heavy transformations upstream (warehouse/dbt) instead of in BI layers
• Use incremental loads for SAP extracts where possible
• Create aggregated tables for high-traffic dashboards
• Implement semantic consistency so caching works as expected
• Monitor refresh and query behavior to catch regressions early

Implementation Checklist: From Idea to Production

Phase 1: Align on outcomes

• Which KPIs must match across Qlik and Power BI?
• Which use cases need near-real-time vs. daily refresh?
• What actions should be possible (drill-through, write-back, alerts)?

Phase 2: Define the integration contract

• Canonical data models (entities and metrics)
• Data freshness SLAs
• Security rules (RLS, masking, entitlements)
• Naming conventions and documentation standards

Phase 3: Build and test

• Develop connectors/extensions with:
• Logging and error handling
• Retry and backoff strategies
• Automated tests (where feasible)

Phase 4: Operationalize

• Monitoring and alerting
• Incident runbooks
• Ownership model (who supports what)
• Change management to prevent breaking dashboards

Common Pitfalls (And How to Avoid Them)

• Pitfall: Building custom connectors too early

Fix: First validate whether a warehouse-first approach removes the need.

• Pitfall: KPI drift between Qlik and Power BI

Fix: Centralize metric logic and publish curated datasets.

• Pitfall: “Shadow IT” extensions with no governance

Fix: Require code review, versioning, and documented release cycles.

• Pitfall: Overloading SAP with analytics queries

Fix: Extract strategically and use SAP drill-through only for operational actions.

Conclusion: One Ecosystem, Multiple Experiences

Connecting Qlik, Power BI, and SAP isn’t about forcing a single tool—it’s about designing a consistent, governed, high-performance data foundation and then letting each platform do what it does best.

With the right mix of custom connectors (secure, reliable access) and custom extensions (better user workflows and interaction), you can create an analytics ecosystem that scales—technically and organizationally.

FAQ: Custom Extensions and Connectors for Qlik, Power BI, and SAP

1) What’s the difference between a connector and an extension in BI tools?

A connector is primarily about data access—authentication, connectivity, API/database communication, and refresh behavior. An extension enhances the front-end experience—custom visuals, interactions, write-back actions, and embedded workflows.

2) Should we connect Power BI and Qlik directly to SAP?

In most cases, it’s better to avoid broad direct querying against SAP for analytics. SAP is optimized for transactions, not high-concurrency BI workloads. A more scalable approach is to extract SAP data into a warehouse/lakehouse, model it, and let both tools query curated datasets—while keeping drill-through to SAP for operational tasks.

3) How do we ensure the numbers match between Qlik and Power BI?

You need shared definitions:

• Standardize business logic in a centralized transformation layer (often in the warehouse)
• Publish curated datasets/marts for consumption
• Use consistent dimension keys and time intelligence rules

The key is eliminating duplicated metric logic inside each BI tool.

4) When is a custom connector worth the investment?

A custom connector is worth it when you need:

• Enterprise authentication (SSO/OAuth variations)
• Fine-grained entitlements and auditing
• Better performance (incremental sync, caching, pagination)
• Stability that generic connectors can’t provide

If a standard connector meets requirements, prefer that—custom work should solve a clear gap.

5) Can we implement row-level security once and reuse it across tools?

Yes—if your architecture supports it. The most reliable method is enforcing row-level security at the data platform/warehouse layer, so both Qlik and Power BI inherit the same restrictions. Tool-specific RLS works too, but it increases the risk of mismatched rules over time.

6) What are the biggest security risks in a mixed Qlik + Power BI + SAP setup?

Common risks include:

• Credentials stored in scripts or desktop files
• Inconsistent access rules across tools
• Untracked exports of sensitive data
• Lack of auditing and lineage

Mitigation: centralized IAM, upstream security enforcement, robust logging, and strong governance.

7) How do we support near-real-time dashboards without breaking performance?

Use a layered approach:

• Ingest incrementally (CDC where possible)
• Store in a system designed for analytical concurrency
• Use aggregates for hot dashboards
• Monitor query patterns and refresh failures

Near-real-time should be engineered as a product with SLAs, not treated as “just refresh more often.”

8) What’s the best way to govern custom extensions so they don’t become “shadow IT”?

Treat extensions like production software:

• Git version control
• Peer reviews and approval workflow
• Release versioning and changelogs
• Automated linting/tests where possible
• Clear ownership and support process

9) Can custom extensions enable write-back to SAP?

They can, but it should be done carefully. In many cases, the safest pattern is:

• Write-back to an intermediate service/workflow layer (with validation and audit logs)
• Sync approved changes to SAP through controlled interfaces

Direct write-back from BI to SAP can introduce security, validation, and operational risks if not engineered properly.

10) How do we choose what to build first: connector, extension, or data model?

Start with the data model and metric definitions (what the business trusts). Then implement the ingestion/connector layer to reliably deliver that data. Extensions come last—once you know which user interactions and workflows actually drive adoption and value.