Self-Service Data Integration

Data Orchestration Layers – Data Orchestration Techniques

Posted on by Nitza Pagan

Data Orchestration Layers

The amount of data orchestration required also depends on the needs of the data processing layers, and it’s important to briefly understand each layer and its role in the orchestration and underlying ETL processes.

Here’s a breakdown of the importance of each data layer and its role in the ETL flows and process:

•     Work area staging: The staging area is used to temporarily store data before it undergoes further processing. It allows for data validation, cleansing, and transformation activities, ensuring data quality and integrity. This layer is essential for preparing data for subsequent stages.

•     Main layer: The main layer typically serves as the central processing hub where data transformations and aggregations take place. It may involve joining multiple data sources, applying complex business rules, and performing calculations. The main layer is responsible for preparing the data for analytical processing.

•     Landing, bronze, gold, and silver layers: These layers represent different stages of data refinement and organization in a data lake or data warehouse environment. The landing layer receives raw, unprocessed data from various sources. The bronze layer involves the initial cleansing and transformation of data, ensuring its accuracy and consistency. The gold layer further refines the data, applying additional business logic and calculations. The silver layer represents highly processed and aggregated data, ready for consumption by end users or downstream systems. Each layer adds value and structure to the data as it progresses through the ETL pipeline.

•     OLAP layer: OLAP is designed for efficient data retrieval and analysis. It organizes data in a multidimensional format, enabling fast querying and slicing-and-dicing capabilities. The OLAP layer optimizes data structures and indexes to facilitate interactive and ad-­ hoc analysis.

Data Movement Optimization: OneLake Data and Its Impact on Modern Data Orchestration One of the heavyweight tasks in data orchestration is around data movement through data pipelines. With the optimization of zones and layers on cloud data platforms, the new data architecture guidance emphasizes minimizing data movement across the platform. The goal is to reduce unnecessary data transfers and duplication, thus optimizing costs and improving overall data processing efficiency.

This approach helps optimize costs by reducing network bandwidth consumption and data transfer fees associated with moving large volumes of data. It also minimizes the risk of data loss, corruption, or inconsistencies that can occur during the transfer process.

Additionally, by keeping data in its original location or minimizing unnecessary duplication, organizations can simplify data management processes. This includes tracking data lineage, maintaining data governance, and ensuring compliance with data protection regulations.

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.

Service-Oriented Architecture (SOA) – Data Orchestration Techniques

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Service-Oriented Architecture (SOA)

Service-oriented architecture (SOA) brought a new paradigm to data integration. SOA allowed systems to expose their functionalities as services, which could be accessed and integrated by other systems through standardized interfaces (e.g., SOAP or REST). This approach enabled greater flexibility and reusability in integrating diverse systems and applications.

There are several tools available that can support the implementation and management of a service-oriented architecture (SOA). Here are some examples:

•     Apache Axis: Axis is a widely used open-source tool for building web services and implementing SOA. It supports various protocols and standards, including SOAP and WSDL, and provides features like message routing, security, and interoperability.

•  Oracle Service Bus: Oracle Service Bus is an enterprise-grade tool

that facilitates the development, management, and integration of services in an SOA environment. It provides capabilities for service mediation, transformation, and routing, as well as message transformation and protocol conversion.

•     IBM Integration Bus (formerly IBM WebSphere Message Broker):

IBM Integration Bus is a powerful integration tool that supports the implementation of SOA and EAI solutions. It provides features for message transformation, routing, and protocol mediation, along with support for various messaging protocols.

•     MuleSoft Anypoint Platform: Anypoint Platform by MuleSoft offers tools and capabilities for implementing SOA and API-based integrations. It includes Anypoint Studio for designing and building services, Anypoint Exchange for discovering and sharing APIs, and Anypoint Runtime Manager for managing and monitoring services.

•     WSO2 Enterprise Integrator: WSO2 Enterprise Integrator is an open-source integration platform that supports building and managing services in an SOA environment. It provides features like message transformation, routing, and security, along with support for various integration patterns and protocols.

•     Microsoft Azure Service Fabric: Azure Service Fabric is a distributed systems platform that can be used for building microservices-based architectures. It provides tools and services for managing and deploying services, as well as features like load balancing, scaling, and monitoring.

These tools offer features and functionalities that can simplify the development, deployment, and management of services in an SOA environment. The choice of tool depends on factors such as the specific requirements of the project, the technology stack being used, and the level of support needed from the vendor or open-source community.

Real-Time and Streaming Data Integration – Data Orchestration Techniques

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Real-Time and Streaming Data Integration

The need for real-time data integration emerged with the proliferation of real-time and streaming data sources. Technologies like change data capture (CDC) and message queues enabled the capture and integration of data changes as they occurred. Stream processing frameworks and event-driven architectures facilitated the real-time processing and integration of streaming data from sources like IoT devices, social media, and sensor networks.

Real-time and streaming data integration tools are designed to process and integrate data as it is generated in real-time or near real-time. These tools enable organizations to ingest, process, and analyze streaming data from various sources. Here are some examples:

•     Apache Kafka: Kafka is a distributed streaming platform that provides high-throughput, fault-tolerant messaging. It allows for real-time data ingestion and processing by enabling the publishing, subscribing, and processing of streams of records in a fault-­ tolerant manner.

•     Confluent Platform: Confluent Platform builds on top of Apache Kafka and provides additional enterprise features for real-time data integration. It offers features such as schema management, connectors for various data sources, and stream processing capabilities through Apache Kafka Streams.

•     Apache Flink: Flink is a powerful stream processing framework that can process and analyze streaming data in real-time. It supports event-time processing and fault tolerance, and offers APIs for building complex streaming data pipelines.

•     Apache NiFi: NiFi is an open-source data integration tool that supports real-time data ingestion, routing, and transformation. It provides a visual interface for designing data flows and supports streaming data processing with low-latency capabilities.

•     Amazon Kinesis: Amazon Kinesis is a managed service by Amazon Web Services (AWS) for real-time data streaming and processing. It provides capabilities for ingesting, processing, and analyzing streaming data at scale. It offers services like Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics.

•     Google Cloud Dataflow: Google Cloud Dataflow is a fully managed service for real-time data processing and batch processing. It provides a unified programming model and supports popular stream processing frameworks like Apache Beam.

•     Azure Stream Analytics: Azure Stream Analytics is a fully managed real-time analytics service that makes it easy to process and analyze streaming data in real-time. It can be used to collect data from a variety of sources, such as sensors, applications, and social media, and then process and analyze that data in real-time to gain insights and make decisions.

•     Structured Streaming: Databricks supports Apache Spark’s Structured Streaming, a high-level streaming API built on Spark SQL. It enables you to write scalable, fault-tolerant stream processing applications. You can define streaming queries using dataframes and datasets, which seamlessly integrate with batch processing workflows.

•     Delta Live Tables: Delta Live Tables is a feature in Databricks that provides an easy way to build real-time applications on top of Delta Lake. It offers abstractions and APIs for stream processing, enabling you to build and store data in delta tables underneath end-to-end streaming applications that leverage the reliability and scalability of delta lakes.

These tools are specifically designed to handle the challenges of real-time and streaming data integration, allowing organizations to process and analyze data as it flows in, enabling real-time insights and actions. The choice of tool depends on factors such as scalability requirements, integration capabilities, programming model preferences, and cloud platform 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.