Data Orchestration in Simple Terms  [+5 Tools]

Data Orchestration in Simple Terms [+5 Tools]

Introduction
Data orchestration has become a pivotal process enabling organizations to effectively collect, transform, integrate and manage data from disparate sources. As businesses increasingly rely on data to drive decisions, products, and processes, the ability to harness data in an organized and efficient manner is crucial. This article will provide an in-depth overview of data orchestration, its key phases, use cases across industries, as well as 5 leading platforms purpose-built for orchestrating complex data workflows.

What is Data Orchestration?
Data orchestration refers to the act of seamlessly coordinating the ingestion, preparation, integration, storage, processing and movement of data from multiple sources across an organization. It helps break down data silos, delivers context around how data relates to other data, and builds an accessible, holistic view of all relevant data.

At its core, data orchestration aims to:

• Streamline the flow of data from diverse sources like databases, cloud apps, APIs etc.
• Transform data into consistent, correctly formatted types
• Establish central access to integrated, high-quality data
• Automate manual tasks through repeatable data pipelines
• Embed governance, compliance and data quality

Benefits of Data Orchestration
Implementing data orchestration confers multiple benefits:

Faster analytics and insights: Integrated, prepared data can be readily analyzed leading to quicker insights.
Enhanced data agility: Swiftly organize and adapt datasets to changing analytical needs.
Improved data quality: Resolve inconsistencies and inaccuracies for reliable analytics.
Greater collaboration: Cross-functional data access enables collaboration between teams.
Risk reduction: Embedded security, privacy and permissions minimize risk.
Higher productivity: Automating tedious manual processes accelerates delivery.
Cost efficiency: Reduced overhead by eliminating redundant data efforts.

Ultimately, data orchestration strengthens the data foundation necessary for driving innovation and competitive advantage.

How Data Orchestration Works
Data orchestration enables the seamless flow of data across the data pipeline – from initial ingestion to preparation, storage, processing and sharing across BI tools, models and apps. Here are key stages orchestrated by data orchestration platforms:

Ingestion: Data is imported from dispersed sources via pre-built or custom connectors into the central orchestration layer.
Preparation: Raw data is cleansed, normalized and transformed into analysis-ready structures.
Enrichment: External data is appended to enrich datasets.
Storage: Prepared data is loaded into dedicated data stores like data warehouses, lakes etc.
Integration: Data scattered in silos is logically integrated providing a unified view.
Processing: Structured workflows enable repeatable aggregation, calculation, filtering and more.
Sharing: Cleansed and processed data is seamlessly channeled into downstream analytics and apps.
Governance: Data access, compliance and use is governed via embedded policies.

With workflows spanning these critical stages abstracted into configurable building blocks, data teams can develop robust data products rapidly. Changes can also propagate efficiently through linked data pipelines.

Phases of Data Orchestration
Now let‘s explore the key phases of data orchestration in detail:

1. Data Collection
   Data orchestration begins by sourcing data from relevant sources across the organization. Based on reporting needs, data is aggregated from databases, cloud apps, ERPs, CSVs and more. Both structured and unstructured data formats are united.

2. Data Ingestion
   Collected raw data is channeled into the orchestration layer via pre-built or configured connectors for centralization. Platforms like Fivetran provide turnkey connectors for common SaaS apps and data warehouses.

3. Data Integration and Transformation
   Disparate datasets streamed into the orchestration tool need to be transformed into an analysis-ready state. Steps like data cleaning, normalization, merging discrete datasets and data model mapping enable analysis.

4. Data Storage and Management
   Following preparation, data is loaded into dedicated analytical data stores like data warehouses, lakes and marts optimized for BI and analytics. Data management involves applying security, access controls and governance policies.

5. Data Processing and Analysis
   Structured data pipelines within orchestration tools manipulate integrated datasets allowing filtering, aggregations, appends, calculations and more. These prepped datasets feed into downstream analysis platforms.

6. Data Movement and Distribution
   Business requirements often necessitate the movement of data from central stores to ancillary systems for departmental analytics, applications, AI/ML model building and more. Data orchestration simplifies distribution.

7. Workflow Management
   Platforms like Prefect embed sophisticated workflow automation ensuring sequential orchestration steps execute reliably without manual oversight. Alerting and visualization provide operational insights.

8. Data Security
   Robust access controls, permissions, transport encryption and anonymization provide data protection while supporting compliance needs.

9. Monitoring and Performance Optimization
   Ongoing monitoring helps identify and resolve bottlenecks across data pipelines and infrastructure lags allowing optimization.

10. Continuous Improvement
    Regular user feedback loops enable enhancement of orchestration tool capabilities and the refinement of configured data pipelines. Agility to evolving needs is bolstered.

Use Cases for Data Orchestration
Data orchestration delivers value across sectors:

E-Commerce: Consolidates transaction data, inventory stats, logistics datasets and CRM data to enable customer segmentation and churn analysis.

Healthcare: Orchestrates patient diagnosis records, lab test results and clinical datasets dispersed across hospitals to build comprehensive views that inform treatment research.

Financial Services: Unites trading logs, risk metrics, client portfolios and compliance data feeds for real-time trade analysis and fraud pattern detection.

Media: Harmonizes viewership data and content metrics from multi-channel distribution pipelines into central data lakes. Enables targeted real-time recommendations.

Manufacturing: Assimilates IoT sensor data across factories, supply chain events from ERPs and equipment failure logs into cloud data lakes. Powers predictive maintenance apps via ML.

Human Resources: Interlinks HR information systems, payroll data, learning management systems and talent data forming a 360-degree employee view that aids retention programs.

Top 5 Data Orchestration Platforms
Manually integrating disparate datasets can be time-intensive. Purpose-built data orchestration platforms help companies accelerate development, minimize errors and apply governance. Here are 5 top orchestration options:

1. Flyte
   An open-source specification-driven workflow orchestration platform designed for complex machine learning and data processing tasks. Key features:

• Centralized interface to monitor data pipelines
• Ability to run independent ML workflows
• Parallel execution harnesses infra elasticity
• Dynamic error handling for uninterrupted workflows
• Scalable via Kubernetes backend

Use cases: MLOps, data science automation, scalable batch processing.

2. Airbyte
   An open-source data integration tool for moving data from sources into warehouses and databases. Key features:

• Pre-configured connectors for common apps.
• Incremental syncs to optimize pipelines
• Data type normalization
• Data testing toolkit
• OpenAPIs to build custom connections

Use cases: Streaming SaaS and database data into cloud data platforms.

3. Prefect

Python-based platform providing workflow organization, monitoring, visualization and infrastructure orchestration. Key features:

• Dynamic mapping of task dependencies
• Centralized dashboard to visualize workflows
• Inline testing framework
• Work queues for parallel execution
• Integrations with Kubernetes, Dask

Use cases: Orchestrating ETL data pipelines, ML workflows.

4. Dagster

An end-to-end data orchestration solution for assets across development, test and production. Key features:

• Integrated data lineage tracking
• CI/CD native through Dagster Cloud
• Advanced unit testing capabilities
• Python and GraphQL API support
• On-premise and multi-cloud deployments

Use cases: Full stack data engineering coverage – ETL, ML pipelines, data ops.

5. Apache Airflow

A popular open source workflow orchestration framework to author, schedule and monitor pipelines. Key features:

• Time and data based pipeline handling
• Visual pipeline authoring canvas
• Pluggable architecture to extend functionality
• Centralized monitoring dashboard
• Role-based access control

Use cases: Traditional ETL/ELT data orchestration, data infrastructure automation

Conclusion
As enterprise data ecosystems grow more complex with data dispersed across siloed sources, orchestrating this data is foundational to deriving value. Data orchestration radically simplifies aggregating relevant datasets across sources, handles key preparation tasks, enforces governance policies, and stages pipelines for downstream utilization.

Leading data teams rely on dedicated data orchestration platforms like Flyte, Airbyte, Prefect and others to develop robust data products faster, efficiently adapt to evolving analytical requirements, and accelerate insights. Beyond the 5 tools discussed here, the space features innovative orchestration startups like Grouparoo, Hightouch, MindsDB and more – each tackling specific aspects across the data lifecycle.

Evaluating options against current vs. future state use cases, IT landscape, team skills and budget is recommended before committing to a platform. But the overarching value of orchestration for managing high-quality, analytics-ready data at scale remains undisputed.