Self-Service Data Integration

Create an AWS Glue Job to Convert the Raw Data into a Delta Table – Data Lake, Lake House, and Delta Lake

Posted on by Nitza Pagan

Create an AWS Glue Job to Convert the Raw Data into a Delta Table

Go to AWS Glue and click on Go to workflows as shown in Figure 3-60. We will get navigated to the AWS Glue studio, where we can create the AWS Glue job to convert the raw CSV file into a delta table.

Figure 3-60.  Go to workflows

Click on Jobs as in Figure 3-61. We need to create the workflow for the job and execute it to convert the raw CSV file into a delta table.

Figure 3-61.  Click on Jobs

We will be using the delta lake connector in the AWS Glue job. We need to activate it.

Go to the Marketplace, as in Figure 3-62.

Figure 3-62.  Click on Marketplace

Search for delta lake as in Figure 3-63, and then click on Delta Lake Connector for AWS Glue.

Figure 3-63.  Activate delta lake connector

Click on Continue to Subscribe as in Figure 3-64.

Figure 3-64.  Continue to Subscribe

Click on Continue to Configuration as in Figure 3-65.

Figure 3-65.  Continue to Configuration

Click on Continue to Launch as in Figure 3-66.

Figure 3-66.  Continue to Launch

Click on Usage instructions as in Figure 3-67. The usage instructions will open up. We have some steps to perform on that page.

Figure 3-67.  Click Usage Instructions

Read the usage instructions once and then click on Activate the Glue connector from AWS Glue Studio, as in Figure 3-68.

Figure 3-68.  Activate Glue connector

Provide a name for the connection, as in Figure 3-69, and then click on Create a connection and activate connector.

Figure 3-69.  Create connection and activate connector

Once the connector gets activated, go back to the AWS Glue studio and click on Jobs, as in Figure 3-70.

Figure 3-70.  Click on Jobs

Select option Visual with a source and target and depict the source as Amazon S3, as in Figure 3-71.

Figure 3-71.  Configure source

Select the target as Delta Lake Connector 1.0.0 for AWS Glue 3.0, as in Figure 3-72. We activated the connector in the marketplace and created a connection earlier. Click on Create.

Figure 3-72.  Configure target

Go to the S3 bucket and copy the S3 URI for the raw-data folder, as in Figure 3-73.

Figure 3-73.  Copy URI

Provide the S3 URI you copied and configure the data source, as in Figure 3-74. Make sure you mark the data format as CSV and check Recursive.

Figure 3-74.  Source settings

Scroll down and check the First line of the source file contains column headers option, as in Figure 3-75.

Figure 3-75.  Source settings

Click on ApplyMapping as in Figure 3-76 and provide the data mappings and correct data types. The target parquet file will be generated with these data types.

Figure 3-76.  Configure mappings

Click on Delta Lake Connector, and then click on Add new option, as in Figure 3-77.

Figure 3-77.  Add new option

Provide Key as path and Value as URI for the delta-lake folder in the S3 bucket, as in Figure 3-78.

Figure 3-78.  Configure target

Go to the Job details as in Figure 3-79 and set IAM Role as the role we created as a prerequisite with all necessary permissions.

Figure 3-79.  Provide IAM role

Click on Save as in Figure 3-80 and then run the job.

Figure 3-80.  Save and run

Once the job runs successfully, go to the delta-lake folder in the S3 bucket. You can see the delta table parquet file generated in Figure 3-81.

Figure 3-81.  Generated delta table in the delta-lake folder

Data Pipelines – Data Orchestration Techniques

Posted on by Nitza Pagan

Data Pipelines

Data pipelines are a series of interconnected steps that move data from one system to another, transforming it along the way to make it suitable for specific use cases. These pipelines can be built using a variety of technologies, including extract, transform, load (ETL) tools; data integration platforms; and cloud-based services, and they form an important component of any data orchestration.

The primary goal of data pipelines is to automate the movement of data, reducing the need for manual intervention and improving the speed and accuracy of data processing. Data pipelines can be used for a wide range of purposes, including data warehousing, data migration, data transformation, and data synchronization.

In today’s digital landscape, data has become the lifeblood of businesses across industries. Organizations are collecting vast amounts of data from various sources, including customer interactions, transactions, sensors, social media, and more. This influx of data provides immense opportunities for extracting valuable insights and driving data-driven decision-making. However, it also presents significant challenges in terms of managing, processing, and deriving meaningful insights from this data.

Data pipelines have emerged as a crucial solution to address these challenges. A data pipeline is a systematic and automated approach to managing the flow of data from its source to its destination. It involves a series of steps, or stages, where data is ingested, processed, transformed, stored, and ultimately delivered to the intended recipients or systems. By establishing a well-designed data pipeline, organizations can streamline and accelerate their data processing workflows, enabling them to extract actionable insights and make informed decisions in a timely manner.

The significance of data pipelines lies in their ability to efficiently handle large volumes of data. With the explosion of data in recent years, organizations are faced with the daunting task of processing and analyzing massive datasets. Traditional manual data

processing methods are no longer sufficient to meet the demands of today’s data-driven world. Data pipelines provide a scalable and automated approach to handle these data volumes, ensuring that data processing is efficient, accurate, and timely.

Furthermore, data pipelines enable organizations to standardize and automate their data workflows. Instead of relying on ad-hoc and manual processes, data pipelines provide a structured framework for data processing, ensuring consistency and repeatability. This standardization not only reduces the chances of errors and inconsistencies but also allows for more efficient collaboration among teams working with the data.

Another significant advantage of data pipelines is their capability to enable real-time and near-real-time analytics. Traditional batch processing methods often involve delays between data collection and analysis. However, with data pipelines, organizations can process data in real-time or near real-time, allowing for immediate insights and rapid decision-making. This is particularly valuable in domains such as finance, e-commerce, and IOT, where timely actions based on fresh data can have a significant impact on business outcomes.

Real-time Processing in Detail – Data Orchestration Techniques

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Real-time Processing in Detail

Real-time processing is a crucial aspect of the data processing workflow, focusing on immediate data processing and delivery. The process begins with data collection, where data is gathered from various sources in real-time. Once collected, the data undergoes a

data cleaning phase to eliminate errors and inconsistencies, ensuring its accuracy and reliability. The next step is data transformation, where the data is converted into a format that is suitable for real-time analysis, enabling prompt insights and actions.

After transformation, the data enters the data processing phase, where it is processed in real-time. This means that the data is acted upon immediately upon receipt, allowing for timely responses and decision-making. Finally, the processed data is delivered to the intended users or stakeholders in real-time, enabling them to take immediate action based on the insights derived from the data.

Real-time processing offers several benefits. It allows for data to be processed as soon as it is received, ensuring up-to-date and actionable information. It is particularly useful for data that requires immediate attention or action. Real-time processing also caters to data that is time sensitive, ensuring that it is analyzed and acted upon in a timely manner.

However, there are challenges associated with real-time processing. It can be expensive to implement and maintain the infrastructure and systems required for real-time processing. Scaling real-time processing to handle large volumes of data can also be challenging, as it requires robust and efficient resources. Additionally, ensuring the availability and reliability of real-time processing systems can be complex, as any downtime or interruptions can impact the timely processing and delivery of data.

In summary, real-time processing plays a vital role in the data processing workflow, emphasizing immediate data processing and delivery. Its benefits include prompt analysis and action based on up-to-date data, which is particularly useful for time-sensitive or critical information. Nevertheless, challenges such as cost, scalability, and system availability need to be addressed to ensure the effective implementation of real-time processing.

Example of Real-time Data Processing with Apache Kafka:

Consider a ride-sharing service that needs to track the real-time location of its drivers to optimize routing and improve customer service. In this scenario, a real-time data processing pipeline is employed. The pipeline continuously ingests and processes the driver location updates as they become available.

The ride-sharing service utilizes a messaging system, such as Apache Kafka, to receive real-time location events from drivers’ mobile devices. The events are immediately processed by the pipeline as they arrive. The processing component of the pipeline may include filtering, enrichment, and aggregation operations.

For example, the pipeline can filter out events that are not relevant for analysis, enrich the events with additional information such as driver ratings or past trip history, and aggregate the data to calculate metrics like average driver speed or estimated time of arrival (ETA).

The processed real-time data can then be used to power various applications and services. For instance, the ride-sharing service can use this data to dynamically update driver positions on the customer-facing mobile app, optimize route calculations in real-­ time, or generate alerts if a driver deviates from the expected route.

Real-time data processing pipelines provide organizations with the ability to respond quickly to changing data, enabling immediate action and providing real-time insights that are essential for time-sensitive applications and services.

One of the ways to implement real-time processing is to design a data pipeline using tools like Apache Kafka, which involves several steps.

Here’s a high-level overview of the process:

•     Identify Data Sources: Determine the data sources you want to collect and analyze. These could be databases, logs, IOT devices, or any other system generating data.

•     Define Data Requirements: Determine what data you need to collect and analyze from the identified sources. Define the data schema, formats, and any transformations required.

•     Install and Configure Apache Kafka: Set up an Apache Kafka cluster to act as the backbone of your data pipeline. Install and configure Kafka brokers, Zookeeper ensemble (if required), and other necessary components.

•     Create Kafka Topics: Define Kafka topics that represent different data streams or categories. Each topic can store related data that will be consumed by specific consumers or analytics applications.

•     Data Ingestion: Develop data producers that will publish data to the Kafka topics. Depending on the data sources, you may need to build connectors or adapters to fetch data and publish it to Kafka.

•     Data Transformation: If necessary, apply any required data transformations or enrichment before storing it in Kafka. For example, you may need to cleanse, aggregate, or enrich the data using tools like Apache Spark, Apache Flink, or Kafka Streams.

•     Data Storage: Configure Kafka to persist data for a certain retention period or size limit. Ensure you have sufficient disk space and choose appropriate Kafka storage settings based on your data volume and retention requirements.

•     Data Consumption: Develop data consumers that subscribe to the Kafka topics and process the incoming data. Consumers can perform various operations, such as real-time analytics, batch processing, or forwarding data to external systems.

•     Data Analysis: Integrate analytics tools or frameworks like Apache Spark, Apache Flink, or Apache Storm to process and analyze the data consumed from Kafka. You can perform aggregations, complex queries, machine learning, or any other required analysis.

•     Data Storage and Visualization: Depending on the output of your data analysis, you may need to store the results in a data store such as a database or a data warehouse. Additionally, visualize the analyzed data using tools like Apache Superset, Tableau, or custom dashboards.

•     Monitoring and Management: Implement monitoring and alerting mechanisms to ensure the health and performance of your data pipeline. Monitor Kafka metrics, consumer lag, data throughput, and overall system performance. Utilize tools like Prometheus, Grafana, or custom monitoring solutions.

•     Scaling and Performance: As your data volume and processing requirements grow, scale your Kafka cluster horizontally by adding more brokers, and fine-tune various Kafka configurations to optimize performance.

It’s important to note that designing a data pipeline using Apache Kafka is a complex task, and the specifics will depend on your specific use case and requirements. It’s recommended to consult the official Apache Kafka documentation and seek expert guidance when implementing a production-grade data pipeline.

Benefits and Advantages of Data Pipelines – Data Orchestration Techniques

Posted on by Nitza Pagan

Benefits and Advantages of Data Pipelines

Data pipelines offer numerous benefits and advantages that enable organizations to effectively manage and process their data. By leveraging data pipelines, organizations can unlock the full potential of their data assets and gain a competitive edge in the following ways:

•     Improved Data Processing Speed and Efficiency: Data pipelines streamline the data processing workflow, automating repetitive tasks and reducing manual intervention. This leads to significant improvements in data processing speed and efficiency. By eliminating time-consuming manual processes, organizations can accelerate data ingestion, processing, and delivery, enabling faster insights and decision-making.

•     Scalability and Handling of Large Data Volumes: With the exponential growth of data, organizations need scalable solutions to handle the increasing data volumes. Data pipelines provide a scalable architecture that can accommodate large amounts of data, ensuring efficient processing without compromising performance. They can handle data in various formats, such as structured, semi-­ structured, and unstructured, allowing organizations to process and analyze diverse data sources effectively.

•     Standardization and Automation of Data Workflows: Data pipelines promote standardization and automation of data workflows, ensuring consistency and repeatability in data processing. By defining clear data pipeline stages, transformations, and validations, organizations can establish standardized processes for handling data. Automation reduces the risk of errors, improves data quality, and enhances productivity by eliminating manual intervention and enforcing predefined rules and best practices.

•     Enables Real-Time and Near-Real-Time Analytics: Traditional batch processing methods often involve delays between data collection and analysis. Data pipelines enable real-time and near-real-time analytics by processing data as it arrives, allowing organizations to gain insights and make timely decisions. Real-time data processing is crucial in domains such as fraud detection, stock trading, IOT sensor data analysis, and customer engagement, where immediate action is required based on fresh data.

•     Facilitates Data Integration and Consolidation: Organizations typically have data spread across multiple systems, databases, and applications. Data pipelines provide a mechanism for efficiently integrating and consolidating data from diverse sources into a unified view. This integration enables organizations to derive comprehensive insights, perform cross-system analysis, and make informed decisions based on a holistic understanding of their data.

•     Enhanced Data Quality and Consistency: Data pipelines facilitate the implementation of data validation and cleansing techniques, improving data quality and consistency. By applying data quality checks, organizations can identify and address data anomalies, inconsistencies, and errors during the data processing stages. This ensures that downstream analytics and decision-making processes are based on accurate and reliable data.

•     Enables Advanced Analytics and Machine Learning: Data pipelines play a critical role in enabling advanced analytics and machine learning initiatives. By providing a structured and automated process for data preparation and transformation, data pipelines ensure that data is the right format and quality for feeding into analytics models. This enables organizations to leverage machine learning algorithms, predictive analytics, and AI-driven insights to derive actionable intelligence from their data.

•     Cost Efficiency and Resource Optimization: Data pipelines optimize resource utilization and reduce operational costs. By automating data processing tasks, organizations can minimize manual effort, streamline resource allocation, and maximize the utilization of computing resources. This helps to optimize costs associated with data storage, processing, and infrastructure, ensuring that resources are allocated efficiently based on actual data processing needs.

Common Use Cases for Data Pipelines – Data Orchestration Techniques

Posted on by Nitza Pagan

Common Use Cases for Data Pipelines

Data pipelines find applications in various industries and domains, enabling organizations to address specific data processing needs and derive valuable insights. Let’s explore some common use cases where data pipelines play a pivotal role:

•     E-commerce Analytics and Customer Insights: E-commerce businesses generate vast amounts of data, including customer interactions, website clicks, transactions, and inventory data. Data pipelines help collect, process, and analyze this data in real-time, providing valuable insights into customer behavior, preferences, and trends. These insights can be used for personalized marketing campaigns, targeted recommendations, inventory management, and fraud detection.

•     Internet of Things (IOT) Data Processing: With the proliferation of IOT devices, organizations are collecting massive volumes of sensor data. Data pipelines are essential for handling and processing this continuous stream of data in real-time. They enable organizations to monitor and analyze IOT sensor data for predictive maintenance, anomaly detection, environmental monitoring, and optimizing operational efficiency.

•     Financial Data Processing and Risk Analysis: Financial institutions deal with a vast amount of transactional and market data. Data pipelines streamline the processing and analysis of this data, enabling real-time monitoring of financial transactions, fraud detection, risk analysis, and compliance reporting. By leveraging data pipelines, financial organizations can make informed decisions, detect anomalies, and mitigate risks effectively.

•     Health Care Data Management and Analysis: The health-care industry generates massive amounts of data, including patient records, medical imaging, sensor data, and clinical trial results. Data pipelines assist in collecting, integrating, and analyzing this data to support clinical research, patient monitoring, disease prediction, and population health management. Data pipelines can also enable interoperability among various health-care systems and facilitate secure data sharing.

•  Social Media Sentiment Analysis and Recommendation Engines:

Social media platforms generate vast amounts of user-generated content, opinions, and sentiments. Data pipelines play a critical role in collecting, processing, and analyzing this data to derive insights into customer sentiment, brand reputation, and social trends. Organizations can leverage these insights for sentiment analysis, social media marketing, personalized recommendations, and social listening.

•     Supply Chain Optimization: Data pipelines are instrumental in optimizing supply chain operations by integrating data from various sources, such as inventory systems, logistics providers, and sales data. By collecting, processing, and analyzing this data, organizations can gain real-time visibility into their supply chain, optimize inventory levels, predict demand patterns, and improve overall supply chain efficiency.

•     Fraud Detection and Security Analytics: Data pipelines are widely used in fraud detection and security analytics applications across industries. By integrating and processing data from multiple sources, such as transaction logs, access logs, and user behavior data, organizations can detect anomalies, identify potential security threats, and take proactive measures to mitigate risks.

•     Data Warehousing and Business Intelligence: Data pipelines play a crucial role in populating data warehouses and enabling business intelligence initiatives. They facilitate the extraction, transformation, and loading (ETL) of data from various operational systems into a centralized data warehouse. By ensuring the timely and accurate transfer of data, data pipelines enable organizations to perform in-­ depth analyses, generate reports, and make data-driven decisions.

These are just a few examples of how data pipelines are utilized across industries. The flexibility and scalability of data pipelines make them suitable for diverse data processing needs, allowing organizations to leverage their data assets to gain valuable insights and drive innovation.

In conclusion, data pipelines offer a wide range of benefits and advantages that empower organizations to efficiently manage and process their data. From improving data processing speed and scalability to enabling real-­time analytics and advanced insights, data pipelines serve as a catalyst for data-driven decision-making and innovation. By embracing data pipelines, organizations can leverage the full potential of their data assets, derive meaningful insights, and stay ahead in today’s data-driven landscape.

Self-Serve Data Platform – Data Mesh

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Self-Serve Data Platform

Each of the domains exposing the data as products needs an infrastructure to host and operate the data. If we take a traditional approach, each of these domains will own their own infrastructure and have their own set of tooling and utilities to handle the data.

This phenomenon is not cost effective and requires a lot of effort from the infrastructure engineers in the domains. This approach also leads to duplicity of efforts across the domain teams.

A better approach would be to build a self-serve platform to facilitate the domains’ storing and exposing data as products. The platform will provide the necessary infrastructure to store and manage the data. The underlying infrastructure will be abstracted to the domain teams. It should expose necessary tooling that will enable the domain teams to manage their data and expose it as their domain’s product to other domain teams. The self-serve platform should clearly define with whom the data should be shared. It should provide an interface to the domain teams so they can manage their data products by using either declarative code or some other convenient manner.

Federated Computational Governance

Data are exposed as products in a data mesh and are owned by the domain teams. The data life cycle is also maintained by the domain teams. However, there is a need for data interoperability across domains. For example, if an organization has a finance domain team and a sales domain team, the finance domain team will need data from the sales domain team, and vice versa. Governance must be defined as to how the data is exchanged, the format of the data, what data can be exposed, network resiliency, security aspects, and so forth. Here, we need Federated Computational Governance to help.

Federated computational governance helps in standardizing the message exchange format across the products. It also helps in automated execution of decisions pertaining to security and access. There will be a set of rules, policies, and standards that has been decided upon by all the domain teams, and there will be automated enforcement of these rules, policies, and standards.

Create Data Products for the Domains – Data Mesh

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Create Data Products for the Domains

Let us design data products for each of the domains.

Create Data Product for Human Resources Domain

We can start with the human resources domain product.

Figure 4-3.  Human resources domain product

Figure 4-3 depicts the human resources domain product. The enterprise uses different human resources applications and portals that perform human resources tasks, like employee attendance, timecards, rewards and recognitions, performance management, and many more. All these human resources data are stored in the HR database built using Azure SQL. The Synapse pipeline reads the data from the HR database, transforms it into a consumable format, and then stores the data in the Synapse SQL pool. The Synapse pipeline can use the Copy activity to fetch the data from the HR database and put it in the Synapse SQL pool after processing the data. The Synapse SQL pool exposes the data to other domain products for consumption. Operational logs and metrics for Synapse are stored in Azure Monitor. Azure Monitor can be used to diagnose any run-time issues in the product. Azure Purview scans data capture and stores the data lineage, data schema, and other metadata information about the data that can help in discovering the data.

Create Data Product for Inventory Domain

Let us now design the inventory domain product.

Figure 4-4.  Inventory resources domain product

Figure 4-4 depicts the inventory domain product. The enterprise uses different inventory applications and portals that manage the inventory of the products that the enterprise develops. These applications add products’ stock and information data, distribution details, and other necessary metadata information. All these inventory data are stored in the inventory database built using Azure SQL. The Synapse pipeline reads the data from the inventory database, transforms it into a consumable format, and then stores the data in an Azure Data Lake Gen2. The Synapse pipeline can use the Copy activity to fetch the data from the HR database and put it in the Azure Data Lake Gen2 after processing it. Azure Data Lake Gen2 exposes the data to other domain products for consumption. Operational logs and metrics for Synapse are stored in Azure Monitor. Azure Monitor can be used to diagnose the run-time issues in the product. Azure Purview scans data capture and stores the data lineage, data schema, and other metadata information about the data that can help in discovering the data.

Create Data Product for Procurement Domain – Data Mesh

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Create Data Product for Procurement Domain

Let us now design the procurement domain product.

Figure 4-5.  Procurement domain product

Figure 4-5 depicts the procurement domain product. There are different suppliers from whom the organization procures data. The data comprise procured product details, prices, and other necessary information. The data is directly ingested from the supplier system, and the data format varies from supplier to supplier. Cosmos DB database is used to store the supplier data. The Synapse pipeline reads the data from the supplier database, transforms the data into a consumable format, and then stores the data in

an Azure Data Lake Gen2. Synapse pipeline can use the Copy activity to fetch the data from HR database and put it in the Azure Data Lake Gen2 after processing the data.

Azure Data Lake Gen2 exposes the data to other domain products for consumption. Operational logs and metrics for Synapse are stored in Azure Monitor. Azure Monitor can be used to diagnose the run-time issues in the product. Azure Purview scans data capture and stores the data lineage, data schema, and other metadata information that can help in discovering the data.

Create Data Product for Sales Domain

Let us now design the sales domain product.

Figure 4-6.  Sales domain product

Figure 4-6 depicts the sales domain product. There are different sales channels for the enterprise. There are distributors that sell the products. There are also B2C sales for the products in the enterprise e-commerce portal. The sales data from multiple channels are ingested into Azure Databricks through Event Hub. The volume of data that is getting ingested is huge. So, the data are stored in Azure Databricks. Data from the inventory domain droduct are consumed by the sales domain product. Spark pipelines transform the data and put the data in an Azure Data Lake Gen2, which exposes the data to other domain products for consumption. Operational logs and metrics for Synapse are stored in Azure Monitor. Azure Monitor can be used to diagnose the run-time issues in the product. Azure Purview scans data capture and stores the data lineage, data schema, and other metadata information that can help in discovering the data.

Create Data Product for Finance Domain – Data Mesh

Posted on by Nitza Pagan

Create Data Product for Finance Domain

Let us now design the finance domain product.

Figure 4-7.  Finance domain product

Figure 4-7 depicts the finance domain product, which consumes data from the inventory, sales, and procurement domain products. The volume of data is huge. Azure Databricks is used to manage the huge amounts of data being ingested. Spark pipelines transform the data and put it in an Azure Data Lake Gen2, which exposes the data to other domain products for consumption. Operational logs and metrics for Synapse are stored in Azure Monitor. Azure Monitor can be used to diagnose the run-time issues in the product. Azure Purview scans data capture and stores the data lineage, data schema, and other metadata information that can help in discovering the data.

Create Self-Serve Data Platform

The self-serve data platform consists of the following components, which give data producers and consumers a self-service experience:

•    Data mesh experience plane

•    Data product experience plane

•    Infrastructure plane

Figure 4-8 depicts the Azure services that can be used to build the self-serve data platform.

Figure 4-8.  Self-serve platform components

Data Mesh Experience Plane

The data mesh experience plane helps customers to discover the data products, explore metadata, see relationships among the data, and so on. The data mesh plane is used by the data governance team to ensure data compliance and best practices by giving them a way to audit and explore the data. Azure Purview can scan the data products and pull the data information from the products, like metadata, data schemas, data lineage, etc.

Data Product Experience Plane

The data product experience plane helps the data producers to add, modify, and delete data in the domain products. Azure Functions can be used in data products to allow data producers to add, modify, or delete the data in the domain product. The Purview catalog in the domain product will expose the data schema definitions and metadata, allowing the data producers to work with the data in the data domain.

Infrastructure Plane

The infrastructure plane helps in self-provisioning of the data domain infrastructure. You can use Azure Resource Manager APIs exposed through Azure Functions to create and destroy infrastructure for the data domain products.

Federated Governance

Azure Policies can be used to bring in federated governance. Data landing zones can be created using Azure Policies that can control and govern API access and activities for the data producers and consumers. Azure Functions and Azure Resource Manager APIs can also be used for governance purposes. Azure Monitor alerts can be used for generating governance alerts and notifications.

Evolution of Data Orchestration – Data Orchestration Techniques

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Evolution of Data Orchestration

In traditional ETL processes, the primary layers consisted of extraction, transformation, and loading, forming a linear and sequential flow. Data was extracted from source systems, transformed to meet specific requirements, and loaded into a target system or data warehouse.

However, these traditional layers had limited flexibility and scalability. To address these shortcomings, the data staging layer was introduced. This dedicated space allowed for temporary data storage and preparation, enabling data validation, cleansing, and transformation before moving to the next processing stage.

The staging layer’s enhanced data quality provided better error handling and recovery and paved the way for more advanced data processing. As data complexity grew, the need for a dedicated data processing and integration layer emerged. This layer focused on tasks like data transformation, enrichment, aggregation, and integration from multiple sources. It incorporated business rules, complex calculations, and data quality checks, enabling more sophisticated data manipulation and preparation.

With the rise of data warehousing and OLAP technologies, the data processing layers evolved further. The data warehousing and OLAP layer supported multidimensional analysis and faster querying, utilizing optimized structures for analytical processing. These layers facilitated complex reporting and ad-hoc analysis, empowering organizations with valuable insights.

The advent of big data and data lakes introduced a new layer specifically designed for storing and processing massive volumes of structured and unstructured data. Data lakes served as repositories for raw and unprocessed data, facilitating iterative and exploratory data processing. This layer enabled data discovery, experimentation, and analytics on diverse datasets, opening doors to new possibilities.

In modern data processing architectures, multiple refinement stages are often included, such as landing, bronze, silver, and gold layers. Each refinement layer represents a different level of data processing, refinement, and aggregation, adding value to the data and providing varying levels of granularity for different user needs. These refinement layers enable efficient data organization, data governance, and improved performance in downstream analysis, ultimately empowering organizations to extract valuable insights from their data.

The modern data processing architecture has made data orchestration efficient and effective with better speed, security, and governance. Here is the brief on the key impacts of modern data processing architecture on data orchestration and ETL:

•     Scalability: The evolution of data processing layers has enhanced the scalability of data orchestration pipelines. The modular structure and specialized layers allow for distributed processing, parallelism, and the ability to handle large volumes of data efficiently.

•     Flexibility: Advanced data processing layers provide flexibility in handling diverse data sources, formats, and requirements. The modular design allows for the addition, modification, or removal of specific layers as per changing business needs. This flexibility enables organizations to adapt their ETL pipelines to evolving data landscapes.

•     Performance Optimization: With specialized layers, data orchestration pipelines can optimize performance at each stage. The separation of data transformation, integration, aggregation, and refinement allows for parallel execution, selective processing, and efficient resource utilization. It leads to improved data processing speed and reduced time to insight.

•     Data Quality and Governance: The inclusion of data staging layers and refinement layers enhances data quality, consistency, and governance. Staging areas allow for data validation and cleansing, reducing the risk of erroneous or incomplete data entering downstream processes. Refinement layers ensure data accuracy, integrity, and adherence to business rules.

•     Advanced Analytics: The availability of data warehousing, OLAP, and Big Data layers enables more advanced analytics capabilities. These layers support complex analytical queries, multidimensional analysis, and integration with machine learning and AI algorithms. They facilitate data-driven decision-making and insight generation.