What is Azure Data Factory?

Cloud-native ETL/ELT service for data integration. Visual pipeline designer, 100+ connectors, mapping data flows for code-free transformations.

What is the difference between ETL and ELT?

ETL transforms data before loading to destination. ELT loads raw data and transforms in target system (Synapse, Databricks). ELT is preferred for cloud.

How many connectors does Data Factory have?

100+ native connectors - cloud services, SaaS applications, databases, file systems. Custom connectors via REST API or ODBC.

What are Mapping Data Flows?

Visual code-free data transformations. Spark-based execution for scale. Join, aggregate, pivot, window functions, conditional splits.

How do you handle real-time data integration?

Event Hub and Stream Analytics for streaming, Data Factory triggers for near-real-time batch, Logic Apps for event-driven integrations.

Do you support hybrid data integration?

Yes, Self-hosted Integration Runtime for on-premise sources. Secure connection via VPN/ExpressRoute without opening firewall.

How do you monitor Data Factory pipelines?

Built-in monitoring dashboard, Azure Monitor integration, custom alerts. Log Analytics for advanced analytics and troubleshooting.

How do you handle errors?

Pipeline activities with retry policies, fault tolerance paths, dead letter queues. Alerting and automated recovery procedures.

What is Change Data Capture (CDC)?

Incremental loading of changes from databases. Data Factory supports CDC from SQL Server, PostgreSQL, MongoDB. More efficient than full loads.

How do you version Data Factory?

Git integration for source control. ARM templates for CI/CD. Branching strategy with dev/test/prod environments.

What are Data Factory costs?

Pay-per-use model - pipeline runs, activity runs, data movement. Data Flows charged per vCore hours. Integration Runtime hours.

Do you support data quality in pipelines?

Yes, data validation activities, quality gates before loading, reject rows handling and quality metrics reporting.

How do you integrate with Synapse?

Synapse has built-in Data Factory capabilities. Shared integration runtimes, seamless data flow to dedicated/serverless SQL pools.

Do you support metadata-driven pipelines?

Yes, parameterized pipelines driven by metadata tables. Dynamic activity generation, reduced maintenance, scalable patterns.

Integracja Danych Azure

Azure Data Integration

Enterprise data integration a ETL/ELT řešení na Azure platformě

Azure Data Factory

Cloud-scale ETL/ELT s 90+ konektory a visual data flows

Synapse Pipelines

Integrované data pipelines v Synapse Analytics

Event-Driven Integration

Real-time integrace s Event Hubs a Event Grid

API Integration

Logic Apps a API Management pro business integration

Hybrid Integration

Self-hosted IR pro on-premises a multi-cloud connectivity

CDC & Streaming

Change data capture a real-time streaming pipelines

Technický deep-dive: Data Integration

Architektonické vzory, best practices a srovnání integračních služeb Azure pro enterprise datové pipeline

Osvědčené postupy pro datovou integraci

ELT over ETL

Preferujte ELT pattern – načtěte surová data do Data Lake/Synapse a transformujte tam. Využijete škálovatelnost cílového systému.

Incremental loading

Používejte Change Data Capture (CDC) nebo watermark pattern místo full load. Dramaticky snižuje objem a čas zpracování.

Metadata-driven pipelines

Navrhujte generické pipeline řízené metadaty (JSON config). Jeden pipeline pro desítky zdrojů místo copy-paste.

Self-hosted IR správně

Self-hosted Integration Runtime na HA clusteru (min. 2 nody). Oddělte pro on-premise a cloud-to-cloud traffic.

Data Lake zóny

Strukturujte Data Lake do Raw/Enriched/Curated zón. Každá zóna má vlastní ACL a retention politiku.

Error handling & retry

Implementujte dead-letter pattern pro failed records. Retry logiku s exponential backoff. Alerting na failures.

Srovnání integračních služeb

Služba	Nejlepší pro	Pricing	Limity
Data Factory	Batch ETL/ELT, orchestrace	Per-pipeline run + DIU	Maximální škálovatelnost
Synapse Pipelines	Analytics-integrated ETL	Per-activity + Synapse compute	Vázáno na Synapse workspace
Logic Apps	Event-driven integrace, B2B	Per-action/connector	Nižší throughput, 400+ konektorů
Event Grid	Event routing, pub/sub	Per-million events	Max 64KB event size
Service Bus	Message queuing, ordering	Per-million operations	Enterprise messaging, sessions

Typické výsledky implementace

100+

Konektory

Nativních konektorů v Data Factory

60%

Úspora dev time

Metadata-driven vs. custom pipeline

<15 min

Data freshness

S CDC a micro-batch ingestion

TB/h

Throughput

S optimalizovaným DIU nastavením

Integrační vzory

Enterprise Data Lake ingestion

Centralizovaná ingestion z 50+ zdrojů (SAP, Salesforce, SQL, APIs) do Data Lake Gen2 s metadatově řízenými pipeline.

Data FactoryMetadata-drivenDelta LakeParameterized

Real-time CDC pipeline

Change Data Capture z SQL Serveru/Oracle přes Event Hubs do Synapse pro near-real-time analytiku.

DebeziumEvent HubsSpark StreamingDelta

B2B integrace

EDI a API integrace s obchodními partnery přes Logic Apps a API Management s transformacemi a validací.

Logic AppsAPIMB2BXML/JSON

IoT data pipeline

Miliony událostí/sec z IoT zařízení přes IoT Hub → Stream Analytics → Data Lake/Synapse/Power BI.

IoT HubStream AnalyticsTime Series Insights

Čeho se vyvarovat

Full load místo incremental

Full load velkých tabulek každou hodinu zabíjí výkon i rozpočet. Implementujte CDC nebo watermark pattern.

Monolitické pipeline

Jeden obří pipeline pro vše je brittle a těžko debugovatelný. Rozdělejte na modulární, znovupoužitelné části.

Hardcoded connections

Connection strings v pipeline definicích jsou security risk. Používejte Azure Key Vault Linked Service.

Ignorování Data Quality

Bez validace dat v pipeline propagujete chyby downstream. Přidejte data quality checks do každé zóny.

Azure Data Integration — klíčová témata

Azure Data Integration — ADF, Event Hubs a hybridní ETL/ELT

Integrace dat napříč cloud a on-premises systémy s Azure Data Factory, Logic Apps a real-time streamingem.

Azure Data Factory pipelines

Code-free ETL/ELT, 90+ konektorů (SAP, Salesforce, Oracle), copy/lookup/foreach/if activities, parameter-driven design.

Mapping Data Flows (Spark)

Vizuální transformace na Spark backendu bez psaní kódu, joins/aggregations/window functions, debug mode, optimizace clusterů.

Self-hosted Integration Runtime

Bezpečné připojení k on-premises SQL/file shares/SAP, vysoká dostupnost (multi-node), firewall friendly (outbound only).

Change Data Capture (CDC)

Native CDC ze SQL Serveru, SAP, Oracle, incremental loads, watermark patterns, MERGE INTO pro target sync.

Event Hubs a Kafka-compatible API

High-throughput ingestion (miliony events/sec), partitions, consumer groups, Kafka protocol pro existující aplikace.

Azure Stream Analytics

SQL-like queries nad streamy z Event Hubs/IoT Hub, windowing (tumbling/hopping/sliding), reference data joins, ML scoring.

Logic Apps a Power Automate

No-code workflow automation, 1000+ konektorů (SaaS, on-prem), schedule/event triggers, error handling, run history.

Azure Service Bus

Reliable enterprise messaging, queues/topics/subscriptions, sessions, dead-letter queues, duplicate detection, FIFO ordering.

API Management pro data APIs

Publikace dat jako REST/GraphQL API, throttling, OAuth/JWT auth, developer portal, monitoring, versioning, mocking.

Schema Registry a Avro evolution

Confluent Schema Registry / Azure Schema Registry, backward/forward compatibility, Avro/JSON/Protobuf, contract-first design.

Dead-letter handling a observability

DLQ pro chybné zprávy, Azure Monitor metrics, Log Analytics queries, alerts na latency/throughput, distributed tracing.

CI/CD pro pipelines (ARM/Bicep)

Azure DevOps/GitHub Actions deployment ADF JSON, parametrizace per env (dev/test/prod), unit tests, integration tests.

Integration Implementation

Strukturovaný přístup k data integration na Azure

Fáze 1: Requirements

1-2 týdny

Source/target mapping
Data flow requirements
Latency requirements
Volume analysis
Error handling needs
SLA definition

Fáze 2: Design

2-3 týdny

Integration architecture
Pipeline design
Error handling strategy
Monitoring design
Security design
DevOps approach

Fáze 3: Development

4-8 týdnů

Pipeline development
Data flows implementation
Testing a validation
Performance tuning
Error handling
Documentation

Fáze 4: Operations

Ongoing

Monitoring a alerting
SLA management
Incident handling
Optimization
Capacity planning
Maintenance

Technology Stack

Azure data integration nástroje

Integration Services

Azure Data FactoryLogic AppsEvent GridService BusAPI Management

Streaming

Event HubsStream AnalyticsApache Kafka (HDInsight)FunctionsIoT Hub

Data Storage

Data Lake Gen2Blob StorageCosmos DBSQL DatabaseSynapse Analytics

DevOps & Monitoring

Azure DevOpsGitHub ActionsAzure MonitorLog AnalyticsApplication Insights

Často kladené otázky o Data Integration

Odpovědi na nejčastější dotazy o data integration na Azure

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