Data Orchestration Layers

The Modern Data Problem and Data Mesh – Data Mesh

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The Modern Data Problem and Data Mesh

Modern organizations keep data in centralized data platforms. Data get generated by multiple sources and are ingested into this data platform. This centralized data platform serves data for analytics and other data consumption needs for the data consumers in the organization. There are multiple departments in an organization, like sales, accounting, procurement, and many more, that can be visualized as domains. Each of these domains ingests data from this centralized data platform. There are data engineers, data administrators, data analysts, and other such roles that work from the centralized data platform. These data professionals work in silos and have very little knowledge of how these data relate to the domain functionality where they were generated. Managing the huge amount of data is cumbersome, and scaling the data for consumption is a big challenge. Making this data reusable and consumable for analytics and machine learning purposes is a big challenge. Storing, managing, processing, and serving the data centrally is monolithic in nature. Figure 4-1 represents the modern data problem. The data is kept in a monolithic centralized data platform that has limited scaling capability, and data is kept and handled in silos.

Figure 4-1.  Modern data problem

The data mesh solves this modern data problem. It is a well-defined architecture or pattern that gives data ownership to the domains or the departments that are generating the data. The domains will generate, store, and manage the data and expose the data to the consumers as a product. This approach makes the data more scalable and discoverable as the data are managed by the domains, and these domains understand the data very well and know what can be done with them. The domains can enforce strict security and compliance mechanisms to secure the data. By adopting a data mesh, we are moving away from the monolithic centralized data platform to a decentralized and distributed data platform where the data are exposed as products and the ownership lies with the domains. Data are organized and exposed by the specific domain that owns the data generation.

A Strong Emphasis on Minimizing Data Duplicity – Data Orchestration Techniques

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A Strong Emphasis on Minimizing Data Duplicity

Overall, the trend of minimizing data movement aligns with the need for cost optimization, data efficiency, and streamlined data workflows in modern data architectures on cloud platforms. By leveraging the appropriate zone and layering strategies, organizations can achieve these benefits and optimize their data processing pipelines.

Zones (OneLake data or delta lake) are another advancement in data processing layers that refers to logical partitions or containers within a data lake or data storage system. They provide segregation and organization of data based on different criteria, such as data source, data type, or data ownership. Microsoft Fabric supports the use of OneLake and delta lakes as storage mechanisms for efficiently managing data zones.

By organizing data into different zones or layers based on its processing status and level of refinement, organizations can limit data movement to only when necessary. The concept of zones, such as landing zones, bronze/silver/gold layers, or trusted zones, allows for incremental data processing and refinement without requiring data to be moved between different storage locations, as well as effective management of data governance and security.

With the advancement of data architecture on cloud platforms, there is a growing emphasis on minimizing data movement. This approach aims to optimize costs and enhance speed in data processing, delivery, and presentation. Cloud platforms like Databricks and the newly introduced Microsoft Fabric support the concept of a unified data lake platform to achieve these goals.

By utilizing a single shared compute layer across services, such as Azure Synapse Analytics, Azure Data Factory, and Power BI, the recently introduced Microsoft Fabric (in preview at the time of writing this book) enables efficient utilization of computational resources. This shared compute layer eliminates the need to move data between different layers or services, reducing costs associated with data replication and transfer.

Furthermore, Microsoft Fabric introduces the concept of linked data sources, allowing the platform to reference data stored in multiple locations, such as Amazon S3, Google Cloud Storage, local servers, or Teams. This capability enables seamless access to data across different platforms as if they were all part of a single data platform. It eliminates the need for copying data from one layer to another, streamlining data orchestration, ETL processes, and pipelines.

Modern data orchestration relies on multiple concepts that work together in data integration to facilitate the acquisition, processing, storage, and delivery of data. Some of the key concepts depend on understanding ETL, ELT, data pipelines, and workflows. Before delving deeper into data integration and data pipelines, let’s explore these major concepts, their origins, and their current usage:

•    ETL and ELT are data integration approaches where ETL involves extracting data, transforming it, and then loading it into a target system, while ELT involves extracting data, loading it into a target system, and then performing transformations within the target system. ETL gained popularity in the early days of data integration for data warehousing and business intelligence, but it faced challenges with scalability and real-time processing. ELT emerged as a response to these challenges, leveraging distributed processing frameworks and cloud-based data repositories. Modern data integration platforms and services offer both ETL and ELT capabilities, and hybrid approaches combining elements of both are also common.

•     Data pipeline refers to a sequence of steps that move and process data from source to target systems. It includes data extraction, transformation, and loading, and can involve various components and technologies, such as batch processing or stream processing frameworks. Data pipelines ensure the smooth flow of data and enable real-time or near-real-time processing.

•     Workflow is a term used to describe the sequence of tasks or actions involved in a data integration or processing process. It defines the logical order in which the steps of a data pipeline or data integration process are executed. Workflows can be designed using visual interfaces or programming languages, and they help automate and manage complex data integration processes. Workflows can include data transformations, dependencies, error handling, and scheduling to ensure the efficient execution of the data integration tasks.

In summary, modern data orchestration encompasses data integration, pipelines, event-driven architectures, stream processing, cloud-based solutions, automation, and data governance. It emphasizes real-time processing, scalability, data quality, and automation to enable organizations to leverage their data assets effectively for insights, decision-making, and business outcomes, letting us understand it better through diving into data integration, data pipelines, ETL, supporting tools, and use cases.

Data Integration – Data Orchestration Techniques

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Data Integration

Data integration is the process of combining data from multiple sources and merging it into a unified and coherent view. It involves gathering data from various systems, databases, files, or applications, regardless of their format, structure, or location, and transforming it into a standardized and consistent format. The goal of data integration is to create a consolidated and comprehensive dataset that can be used for analysis, reporting, and decision-making.

Data integration involves several steps, including data extraction, data transformation, and data loading. In the extraction phase, data is collected from different sources using various methods, such as direct connections, APIs, file transfers, or data replication. The extracted data is then transformed by cleaning, validating, and structuring it to ensure consistency and accuracy. This may involve performing data quality checks, resolving inconsistencies, and standardizing data formats. Finally, the transformed data is loaded into a central repository, such as a data warehouse or a data lake, where it can be accessed, queried, and analyzed.

Data integration is essential because organizations often have data stored in different systems or departments, making it difficult to gain a holistic view of their data assets. By integrating data, businesses can break down data silos, eliminate duplicate or redundant information, and enable a comprehensive analysis of their operations, customers, and performance. It provides a unified view of data, enabling organizations to make informed decisions, identify trends, and uncover valuable insights.

In the early days of data integration, manual methods such as data entry, file transfers, and manual data transformations were prevalent. These approaches were time-consuming, error-prone, and not scalable. Data integration has evolved significantly over the years to address the increasing complexity and diversity of data sources and systems. There are various approaches to data integration, including manual data entry, custom scripting, and the use of specialized data integration tools or platforms. These tools often provide features such as data mapping, data transformation, data cleansing, and data synchronization, which streamline the integration process and automate repetitive tasks.

With the rise of relational databases and structured data, batch processing emerged as a common data integration technique. It involved extracting data from source systems, transforming it, and loading it into a target system in batches. Batch processing was suitable for scenarios where real-time data integration was not necessary.

Enterprise Application Integration (EAI) – Data Orchestration Techniques

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Enterprise Application Integration (EAI)

Enterprise application integration (EAI) emerged as a comprehensive approach to data integration. It aimed to integrate various enterprise applications and systems, such

as ERP, CRM, and legacy systems, by providing a middleware layer and standardized interfaces. EAI solutions enabled seamless data sharing and process coordination across different applications.

EAI tools include the following:

•     IBM Integration Bus: Formerly known as IBM WebSphere Message Broker, IBM Integration Bus is an EAI tool that enables the integration of diverse applications and data sources. It provides a flexible and scalable platform for message transformation, routing, and data mapping.

•     MuleSoft Anypoint Platform: MuleSoft’s Anypoint Platform offers EAI capabilities through components like Anypoint Studio and Anypoint Connectors. It allows organizations to connect and integrate applications, systems, and APIs, and provides features for data mapping, transformation, and orchestration.

•     Oracle Fusion Middleware: Oracle’s Fusion Middleware platform includes various tools and technologies for enterprise application integration, such as Oracle Service Bus, Oracle BPEL Process Manager, and Oracle SOA Suite. It enables organizations to integrate applications, services, and processes across different systems.

•     SAP NetWeaver Process Integration (PI): SAP PI is an EAI tool provided by SAP that facilitates the integration of SAP and non-SAP applications. It offers features for message routing, transformation, and protocol conversion, and supports various communication protocols and standards.

•     TIBCO ActiveMatrix BusinessWorks: TIBCO’s ActiveMatrix BusinessWorks is an EAI platform that allows organizations to integrate applications, services, and data sources. It provides a graphical interface for designing and implementing integration processes and supports a wide range of connectivity options.

•     Dell Boomi: Boomi, a part of Dell Technologies, offers a cloud-based EAI platform that enables organizations to connect and integrate applications, data sources, and devices. It provides features for data mapping, transformation, and workflow automation.

Data Warehousing – Data Orchestration Techniques

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Data Warehousing

Data warehousing became popular as a means of integrating and consolidating data from various sources into a centralized repository. Data was extracted, transformed, and loaded into a data warehouse, where it could be analyzed and accessed by business intelligence (BI) tools. Data warehousing facilitated reporting, analytics, and decision-­ making based on integrated data.

There are several popular tools and platforms available for data warehousing that facilitate the design, development, and management of data warehouse environments. Here are some examples:

•     Amazon Redshift: Redshift is a fully managed data warehousing service provided by Amazon Web Services (AWS). It is designed for high-performance analytics and offers columnar storage, parallel query execution, and integration with other AWS services.

•     Snowflake: Snowflake is a cloud-based data warehousing platform known for its elasticity and scalability. It separates compute and storage, allowing users to scale resources independently. It offers features like automatic optimization, near-zero maintenance, and support for structured and semi-structured data.

•     Microsoft Azure Synapse Analytics: Formerly known as Azure SQL Data Warehouse, Azure Synapse Analytics is a cloud-based analytics service that combines data warehousing, Big Data integration, and data integration capabilities. It integrates with other Azure services and provides powerful querying and analytics capabilities.

•     Google BigQuery: BigQuery is a fully managed serverless data warehouse provided by Google Cloud Platform (GCP). It offers high scalability, fast query execution, and seamless integration with other GCP services. BigQuery supports standard SQL and has built-in machine learning capabilities.

•     Oracle Autonomous Data Warehouse: Oracle’s Autonomous Data Warehouse is a cloud-based data warehousing service that uses artificial intelligence and machine learning to automate various management tasks. It provides high-performance, self-tuning, and self-securing capabilities.

•     Teradata Vantage: Teradata Vantage is an advanced analytics platform that includes data warehousing capabilities. It provides scalable parallel processing and advanced analytics functions, and supports hybrid cloud environments.

•     Delta Lake: A delta lake is an open-source storage layer built on top of Apache Spark that provides data warehousing capabilities. It offers ACID (Atomicity, Consistency, Isolation, Durability) transactions, schema enforcement, and data reliability for both batch and streaming data. Delta lakes enable you to build data pipelines with structured and semi-structured data, ensuring data integrity and consistency.

These tools offer a range of features and capabilities for data warehousing, including data storage, data management, query optimization, scalability, and integration with other systems. The choice of tool depends on specific requirements, such as the scale of data, performance needs, integration needs, and cloud provider preferences.

Cloud-Based Data Integration – Data Orchestration Techniques

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Cloud-Based Data Integration

Cloud computing has revolutionized data integration by offering scalable infrastructure and cloud-based integration platforms. Cloud-based data integration solutions provide capabilities such as data replication, data synchronization, and data virtualization, enabling seamless integration between on-premises and cloud-based systems.

There are several cloud-based data integration tools available that provide seamless integration and data management in cloud environments. Some popular examples include:

•     AWS Glue: It is a fully managed extract, transform, and load (ETL) service provided by Amazon Web Services (AWS). It enables users to prepare and transform data for analytics, and it integrates well with other AWS services.

•     Microsoft Azure Data Factory: This cloud-based data integration service by Microsoft Azure allows users to create data-driven workflows to orchestrate and automate data movement and transformation. It supports a wide range of data sources and destinations.

•     Google Cloud Data Fusion: It is a fully managed data integration service on Google Cloud Platform (GCP) that simplifies the process of building and managing ETL pipelines. It provides a visual interface for designing data flows and supports integration with various data sources.

•     Informatica Cloud: Informatica offers a cloud-based data integration platform that enables users to integrate and manage data across on-premises and cloud environments. It provides features like data mapping, transformation, and data quality management.

•     SnapLogic: SnapLogic is a cloud-based integration platform that allows users to connect and integrate various data sources, applications, and APIs. It offers a visual interface for designing data pipelines and supports real-time data integration.

These cloud-based data integration tools provide scalable and flexible solutions for managing data integration processes in cloud environments, enabling organizations to leverage the benefits of cloud computing for their data integration needs.

Data Integration for Big Data and NoSQL – Data Orchestration Techniques

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Data Integration for Big Data and NoSQL

The emergence of Big Data and NoSQL technologies posed new challenges for data integration. Traditional approaches struggled to handle the volume, variety, and velocity of Big Data. New techniques, like Big Data integration platforms and data lakes, were developed to enable the integration of structured, semi-structured, and unstructured data from diverse sources.

When it comes to data integration for Big Data and NoSQL environments, there are several tools available that can help you streamline the process. Apart from Apache Kafka and Apache NiFi already described, some of the other tools that may be considered are the following:

•     Apache Spark and Databricks: Spark is a powerful distributed processing engine that includes libraries for various tasks, including data integration. It provides Spark SQL, which allows you to query and manipulate structured and semi-structured data from different sources, including NoSQL databases.

•  Talend: Talend is a comprehensive data integration platform that

supports Big Data and NoSQL integration. It provides a visual interface for designing data integration workflows, including connectors for popular NoSQL databases like MongoDB, Cassandra, and HBase.

•     Pentaho Data Integration: Pentaho Data Integration (PDI), also known as Kettle, is an open-source ETL (extract, transform, load) tool. It offers a graphical environment for building data integration processes and supports integration with various Big Data platforms and NoSQL databases.

•     Apache Sqoop: Sqoop is a command-line tool specifically designed for efficiently transferring bulk data between Apache Hadoop and structured data stores, such as relational databases. It can be used to integrate data from relational databases to NoSQL databases in a Hadoop ecosystem.

•     StreamSets: StreamSets is a modern data integration platform that focuses on real-time data movement and integration. It offers a visual interface for designing data pipelines and supports integration with various Big Data technologies and NoSQL databases.

Use Cases – Data Orchestration Techniques

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Use Cases

In current usage, data integration is employed in various scenarios, including the following:

•     Business Intelligence and Analytics: Data integration enables the consolidation of data from multiple sources to create a unified view for analysis, reporting, and decision-making.

•     Data Migration and System Consolidation: When organizations undergo system upgrades, mergers, or acquisitions, data integration is crucial for migrating data from legacy systems to new platforms or consolidating data from multiple systems into a single unified system.

•     Data Governance and Master Data Management (MDM): Data integration is essential for establishing data governance practices and implementing master data management strategies. It ensures data consistency, accuracy, and reliability by integrating and harmonizing data across different systems and applications.

•     Data Sharing and Collaboration: Data integration enables seamless sharing and collaboration of data between different departments, teams, or partner organizations. It allows for real-time data exchange and synchronization, facilitating collaboration on shared datasets or joint projects.

•     Data Integration in the Cloud: Cloud-based data integration solutions are widely used to integrate data across on-premises systems, cloud-based applications, and Software-as-a-Service (SaaS) platforms. It provides scalability, flexibility, and cost-efficiency, allowing organizations to leverage cloud-based infrastructure and services for data integration.

•     Real-time Data Integration and Stream Processing: With the increasing availability of streaming data sources, real-time data integration and stream processing techniques are used to capture, process, and integrate data as it flows in real-time. This enables organizations to react quickly to events, make real-time decisions, and perform continuous analytics.

•     Internet of Things (IOT) Data Integration: IOT devices generate vast amounts of data, and data integration is crucial for analyzing this data in real-time. Data integration techniques are employed to connect IoT devices, capture sensor data, and integrate it with other enterprise systems for real-time monitoring, predictive maintenance, and operational optimization.

•     Data Integration for Data Lakes and Big Data: Data integration is used to ingest, process, and integrate diverse data sources into data lakes and Big Data platforms. It enables organizations to combine structured and unstructured data from various sources for advanced analytics, machine learning, and data exploration.

•     Data Integration for Data Science and AI: Data integration plays a vital role in data science and AI initiatives by integrating and preparing data for model training, feature engineering, and predictive analytics. It involves integrating data from different sources, cleaning and transforming data, and creating curated datasets for analysis and modeling.

In summary, data integration has evolved to accommodate the complexities of modern data ecosystems. It is employed in various domains, including business intelligence, data migration, data governance, cloud-based integration, real-time processing, IOT, big data, and AI. These applications enable organizations to unlock the value of their data, make informed decisions, and drive business success.