Create Data Product for Finance Domain

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.

Data Orchestration Layers – Data Orchestration Techniques

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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.

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.

Middleware and ETL Tools – Data Orchestration Techniques

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Middleware and ETL Tools

The advent of middleware and extract, transform, load (ETL) tools brought significant advancements in data integration. Middleware technologies, like message-oriented middleware (MOM), enabled asynchronous communication between systems, facilitating data exchange. ETL tools automated the process of extracting data from source systems, applying transformations, and loading it into target systems.

Middleware and ETL tools widely used include the following:

•     IBM WebSphere MQ: This is a messaging middleware that enables communication between various applications and systems by facilitating the reliable exchange of messages.

•     Oracle Fusion Middleware: This middleware platform from Oracle offers a range of tools and services for developing, deploying, and integrating enterprise applications. It includes components like Oracle SOA Suite, Oracle Service Bus, and Oracle BPEL Process Manager.

•     MuleSoft Anypoint Platform: MuleSoft provides a comprehensive integration platform that includes Anypoint Runtime Manager and Anypoint Studio. It allows organizations to connect and integrate applications, data, and devices across different systems and APIs.

•     Apache Kafka: Kafka is a distributed messaging system that acts as a publish-subscribe platform, providing high-throughput, fault-­ tolerant messaging between applications. It is widely used for building real-time streaming data pipelines.

ETL Tools include the following:

•     Informatica PowerCenter: PowerCenter is a popular ETL tool that enables organizations to extract data from various sources, transform it based on business rules, and load it into target systems. It offers a visual interface for designing and managing ETL workflows.

•     IBM Infosphere DataStage: DataStage is an ETL tool provided by IBM that allows users to extract, transform, and load data from multiple sources into target systems. It supports complex data transformations and provides advanced data integration capabilities.

•     Microsoft SQL Server Integration Services (SSIS): SSIS is a powerful ETL tool included with Microsoft SQL Server. It provides a visual development environment for designing ETL workflows and supports various data integration tasks.

•     Talend Data Integration: Talend offers a comprehensive data integration platform that includes Talend Open Studio and Talend Data Management Platform. It supports ETL processes, data quality management, and data governance.

These examples represent a subset of the wide range of middleware and ETL tools available in the market. Each tool has its own set of features and capabilities, allowing organizations to choose the one that best fits their specific integration and data processing requirements.

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.

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.

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.

Self-Service Data Integration – Data Orchestration Techniques

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Self-Service Data Integration

Modern data integration solutions often emphasize self-service capabilities that empower business users and data analysts to perform data integration tasks without heavy reliance on IT teams. Self-service data integration tools provide intuitive interfaces, visual data mapping, and pre-built connectors to simplify and accelerate the integration process.

The following are some of the examples of a self-service data integration tool:

•     Apache NiFi: NiFi is an open-source data integration and workflow automation tool that allows users to design and execute data flows across various systems. It provides a web-based interface with a drag-and-drop visual interface, making it easy for users to create and manage data integration processes without extensive coding knowledge.

•    Another example is Talend Open Studio, which is a comprehensive data integration platform that offers a graphical interface

for designing and deploying data integration workflows. It supports various data integration tasks, such as data extraction, transformation, and loading (ETL), as well as data quality management.

The following are a few of the other newly introduced tools that are getting popular:

•     Hevo Data is a cloud-based data integration platform that allows users to connect to and integrate data from over 149 data sources, including databases, cloud storage, Software as a Service (SaaS) applications, and more. Hevo Data offers a drag-and-drop interface that makes it easy to create data pipelines without writing any code.

•     SnapLogic is another cloud-based data integration platform that offers a visual drag-and-drop interface for creating data pipelines. SnapLogic also offers a wide range of pre-built connectors for popular data sources, making it easy to connect to your data quickly.

•     Jitterbit is a self-service data integration platform that offers a variety of features, including data mapping, data transformation, and data quality checking. Jitterbit also offers a wide range of pre-built connectors for popular data sources.

•     Celigo is a self-service data integration platform that focuses on integrating data from SaaS applications. Celigo offers a variety of pre-­ built connectors for popular SaaS applications, as well as a drag-and-­ drop interface for creating data pipelines.

•     Zapier is a no-code data integration platform that allows users to connect to and automate workflows between different apps and services. Zapier offers a wide range of pre-built integrations, as well as a visual interface for creating custom integrations.

One of the other tools to be understood is Microsoft Fabric, as it’s a newly built end-to-end data analytics tool that is more like an ecosystem of data processing, storage, and sharing rather than just a data integration tool. These self-service data integration tools empower users to independently integrate data from multiple sources, apply transformations, and load it into target systems, all without relying heavily on IT or development teams.