Data Architect

Data Architects: Building the Foundation for Data Use.

• BLUF: Data architects are (1) the masterminds behind an organization's data infrastructure. (2) They combine technical expertise with an understanding of business needs to design, plan, and implement systems for storing, managing, and accessing data.

• R&R (Example): Create blueprint for how data is stored, accessed and used. Design and implement solutions for integrating data from various sources. Analyze, plan, and define the data architecture framework, including security, reference data, metadata, and master data. Create and implement data management processes and procedures. Collaborate with other teams to create and implement data strategy. Develop strategies for data acquisition, migration and recovery. Ability to identify and resolve performance issues related to storage, memory and retrieval. Strong Data management skills and experience – knowledge of CDMP or one such framework is an advantage. Strong Data modelling skills with knowledge on SQL development. Knowledge on implementing and managing database systems, data warehouses. Knowledge on design of data pipelines, orchestrations and models. Knowledge of data governance practices and tools like Collibra is an added advantage. Knowledge of SAP S/4, CDS views, SDI/SDA/SLT/BODS, SAP CPI, Cloud connector, API, HANA Cloud/HANA Modeling, programming languages like python, SQL, strong RDBMS, modelling skills. Knowledge of SAC , BOBJ or any data analytics and reporting tools.

• Key Roles (What they do): (4)

  1. Translating Business Needs: Data architects act as interpreters, converting business goals into technical requirements for data storage and retrieval.

  2. Designing the Blueprint: They craft the data architecture, which serves as a blueprint for the entire data management system. This includes selecting appropriate technologies, like databases and data warehouses, and outlining data flow throughout the organization.

  3. Ensuring Data Quality and Security: Data architects establish procedures to ensure data accuracy, consistency, and security. They implement data governance practices to regulate data access and usage.

  4. Building for the Future: Data architects plan for scalability and future needs, ensuring the data infrastructure can adapt to evolving business requirements.

• Step-by-Step Strategic Process Approach:.(6)

  1. Business Needs Analysis: Understanding the organization's goals and data usage patterns is crucial.

  2. Current State Assessment: Data architects evaluate existing data sources, storage systems, and any challenges faced.

  3. Data Modeling: This involves defining the structure and organization of the data to be stored.

  4. Technology Selection: The architect chooses the most suitable data management technologies based on the data and business needs.

  5. Implementation and Testing: The designed architecture is built and rigorously tested for functionality, performance, and security.

  6. Ongoing Management and Optimization: The data architecture requires monitoring, maintenance, and adjustments to ensure it remains effective over time.

• Azure tools for Data Architects. (4)

  1. Azure SQL Database: A managed relational database service for storing and querying structured data.

  2. Azure Synapse Analytics: A cloud-based data warehousing solution for large-scale data analytics.

  3. Azure Data Factory: A data integration service for automating data movement and transformation processes.

  4. Azure Databricks: An Apache Spark-based analytics platform for large-scale data processing and ML.

• Duties: 3 Common Tasks.

  1. Designing and Implementing Data Management Solutions: This involves selecting technologies, building databases, and establishing data governance practices.

  2. Ensuring Data Quality and Security: Data architects implement data quality checks, define access controls, and maintain data security measures.

  3. Collaborating with Stakeholders: They work closely with business teams to understand their needs, translate them into technical requirements, and ensure the data architecture serves its purpose.

(E)

Empirical data -- refers to information that is gathered through direct observation or experimentation. Data is based on real-world facts (explicit knowledge), NOT on opinion, theory, or speculation. -- Example -- Think of it like this: if you want to know how hot your coffee is, you wouldn't just guess or read about coffee temperatures. You would use your senses to gather empirical data by dipping your finger in the cup or using a thermometer.

• VALUE: (4)

1. Objectivity: It is based on facts and observations, not personal opinions or beliefs.

2. Measurability: It can be quantified and recorded in a way that is consistent and repeatable.

3. Reproducibility: The data can be collected and analyzed by others under the same conditions and should produce the same results.

4. Relevance: It is relevant to the question or problem being investigated.

Azure tools for Big Data.

• BLUF: The best Azure tools for you depend on your specific data types, processing needs, and budget. Start by understanding your data landscape and consider seeking expert guidance to build the optimal Azure data platform for your unique requirements.

• Azure tools from (1) Data Ingestion (2) Data Storage (3) Data Processing, to (4) Data Analysis & Visualization -- (6):

  1. Data Ingestion: (3)

     • Azure Data Factory: Orchestrates data movement across various sources, on-premises or cloud, to your designated Azure storage. Think of it as a pipeline builder for data flow.

     • Azure Event Hub: Handles high-volume data streams in real-time, ideal for IoT or sensor data ingestion. It acts as a buffer and distributor for continuous data flow.

     • Azure Databricks: Provides a unified platform for ingesting, processing, and analyzing large datasets using Apache Spark. Imagine an all-in-one environment for complex data wrangling.

  2. Data Storage: (3)

     • Azure Data Lake Store: A scalable and cost-effective repository for all your raw, unstructured data, like logs, images, or social media feeds. Think of it as a giant, open container for any type of data.

     • Azure Blob Storage: Highly durable and scalable object storage for large files and unstructured data. Think of it as a secure, flexible bin for bulky data.

     • Azure Cosmos DB: Globally distributed NoSQL database for high-performance, globally-scaled applications. Think of it as a flexible, responsive database that can handle massive data volumes and diverse data models.

  3. Data Processing: (3)

     • Azure HD Insight: A managed Apache Hadoop and Spark service for large-scale data processing and analytics. Imagine a powerful engine for crunching and analyzing massive datasets using established open-source frameworks.

     • Azure Synapse Analytics: Provides integrated data warehousing and analytics capabilities for structured, semi-structured, and unstructured data. Think of it as a one-stop shop for analyzing data from various sources, offering both storage and processing power.

     • Azure Data Explorer: Ideal for analyzing large volumes of time-series data from diverse sources, like logs, metrics, and sensor data. Think of it as a powerful telescope for exploring and analyzing data patterns over time.

  4. Data Analysis and Visualization: (3)

     • Azure Power BI: Interactive data visualization tool for creating reports and dashboards to gain insights from your data. Think of it as a paintbrush for transforming data into understandable visual stories.

     • Azure Machine Learning (ML): To build, deploy, and manage ML models for predictive analytics and insights. Think of it as a laboratory for extracting hidden knowledge from your data.

  5. Azure Data Catalog: This creates a searchable inventory of your data assets, making it easier to find and understand what data you have. Think of it as a library catalog for your data, helping you navigate and utilize your datasets effectively.

  6. Azure Data Share: Securely shares data with partners and external organizations, while maintaining control and governance. Think of it as a secure bridge for data collaboration.

What is Data Management. 

• Refers to the fundamental principles and practices that organizations use to effectively collect, store, organize, manage, and secure data assets. 

• These concepts are crucial for ensuring data quality, consistency, and accessibility, and play a significant role in decision-making, operational efficiency, and regulatory compliance. 

• These concepts serve as a foundation for establishing a robust and efficient data management strategy that aligns with an organization's goals and objectives.

Data Management Concepts: (10)

1. Data governance: Establishing policies, procedures, and accountability frameworks for effective data management within an organization.

   • Federal Data Governance (5)-- BLUF: Implementing federal data governance involves a multi-layered approach with various steps. It is an ongoing process. It requires commitment, leadership, and continuous improvement to maximize the value and benefits of data for the public good. -- Key actions are (5): -- (1) -- Establish a Foundation (3): (1-1) Develop a data governance strategy: Align with the Federal Data Strategy (FDS) principles and identify specific goals, roles, responsibilities, and success metrics. (1-2) Designate a Chief Data Officer (CDO) or lead: This individual champions data governance initiatives and oversees implementation. (1-3) Establish a Data Governance Council/Committee: Comprised of representatives across agencies to define policies, standards, and best practices. --  (2) -- Inventory and Classify Data (3): (2-1) Conduct a comprehensive data inventory: Identify all data assets within the agency, including their location, format, and sensitivity. (2-2) Classify data based on sensitivity and risk: Establish categories like public, sensitive, confidential, etc., with access controls and security measures. (2-3) Document data characteristics and usage: Include information like purpose, collection methods, and retention schedules. -- (3) -- Implement Policies and Standards (3): (3-1) Define data quality standards: Set expectations for accuracy, completeness, consistency, and timeliness. (3-2) Establish data access and security controls: Determine who can access, modify, and use data based on authorization levels. (3-3) Develop data privacy and protection policies: Ensure compliance with relevant regulations and ethical principles. -- (4) -- Build Data Management Capabilities (3): (4-1) Invest in data governance tools and technologies: This can include data catalogs, metadata management systems, and access control software. (4-2) Train staff on data governance practices: Equip employees with skills to manage data effectively and responsibly. (4-3) Promote data stewardship: Identify individuals responsible for specific data assets and their quality, integrity, and use. -- (5) -- Monitor and Evaluate: (5-1) Establish metrics to track progress towards data governance goals. (5-2) Regularly review and update policies, standards, and procedures. (5-3) Conduct audits and assessments to identify and address weaknesses.

2. Data architecture: Defining and structuring the data assets, including data models, schemas, and storage mechanisms.

3. Data integration: Combining data from various sources and systems into a unified and consistent format for analysis and reporting.

4. Data quality: Ensuring the accuracy, completeness, and reliability of data through validation, cleansing, and standardization processes.

5. Data security: Protecting data from unauthorized access, misuse, or loss through encryption, access controls, and backup mechanisms.

6. Data privacy: Implementing measures to comply with regulations and protect individuals' personally identifiable information (PII).

7. Master data management: Managing critical data elements (such as customer or product information) across different systems and applications to ensure consistency and accuracy.

8. Data lifecycle management: Managing data from its creation to its archival or deletion, ensuring appropriate retention periods and disposal processes.

9. Metadata management: Capturing and managing metadata (data about data) to enable efficient data discovery, understanding, and usage.

10. Data analytics: Extracting insights and value from data through various techniques, such as data mining, statistical analysis, and machine learning.

Data Management Principles. (6)

• BLUF: Data Management Principles outlines the core values that guide an organization to collect, store, protect, and utilize data. These principles ensure your data is reliable, accessible, secure, and ultimately delivers value to the organization.

• Key principles (6): (1) Create a data management strategy: This involves defining your data governance framework, establishing goals, and outlining how you'll achieve them. (2) Define roles and responsibilities: Clear ownership and accountability for data quality, security, and accessibility are crucial. (3) Control data throughout its lifecycle: Implement processes for data creation, storage, access, usage, archiving, and destruction. (4) Ensure data quality: Maintain accurate, complete, and consistent data through validation, cleansing, and monitoring. (5) Collect and analyze metadata: Document data characteristics and lineage for better understanding and utilization. (6) Maximize data use: Foster a data-driven culture by promoting data accessibility and analysis for informed decision-making.

• Comply with regulations: Adhere to all relevant data privacy and security regulations.

Data Management Strategy.

• General framework (7): (1) Assess your current state: Evaluate your existing data practices, infrastructure, and challenges. (2) Define your data goals: Identify how you want to use data to achieve organizational objectives. (3) Catalog your data assets: Understand what data you have, where it resides, and how it's used. (4) Develop data governance policies: Establish rules and procedures for data access, security, and quality. (5) Choose data management tools and technologies: Select solutions that align with your strategy and budget. (6) Implement your plan: Execute your strategy in phases, starting with high-impact areas. (7) Monitor and measure success: Track key metrics and adapt your strategy as needed.

• Or, use Tableau's 5 Key Steps to Creating a Data Management Strategy: https://www.tableau.com/learn/articles/data-management-strategy 

AuthS: (3)

1. Disciplined Agile Data Management by Project Management Institute: https://dotnettutorials.net/lesson/data-management-approaches/  

2. Five Key Steps to Creating a Data Management Strategy by Tableau: https://www.tableau.com/learn/articles/data-management-strategy 

3. Customer Data Management: 6 Principles to Perfect Your CDM: https://segment.com/docs/guides/

BLUF -- OLAP and OLTP manage and analyze data. They are two different approaches used to manage and analyze data.

• OLAP (Online Analytical Processing): It is a technology that enables users to analyze large sets of data from multiple dimensions. OLAP systems are designed to support complex analytical and ad-hoc queries, including data aggregations, drill-downs, and pivot tables. OLAP databases typically store summarized, historical data and are optimized for read-heavy workloads.

• OLTP (Online Transaction Processing): It is a technology used to manage transactional and operational data in real-time. OLTP systems are designed to handle high volumes of small, transactional data operations, such as insert, update, and delete. They prioritize data integrity, consistency, and concurrency.

Azure services that support OLAP/OLTP workloads, such as -- (4):

1. Azure Analysis Services : (Single) It is a fully managed platform as a service (PaaS) offering that provides OLAP capabilities for multidimensional and tabular models. It allows for interactive analysis and reporting.

2. Azure SQL Database: (Single) It is a managed relational database service that primarily focuses on OLTP workloads. It offers scalability, high availability, and security features required for handling transactional data.

3. Azure Synapse Analytics: (Both) This service offers both OLAP & OLTP capabilities. It allows users to perform advanced analytics on large datasets using OLAP techniques. It also provides capabilities for real-time transactional processing using its integrated SQL engine.

4. Azure SQL Data Warehouse: (Both) It is a fully managed and massively parallel processing (MPP) data warehouse service that supports both OLAP and OLTP workloads. It provides scalability for analytical workloads and can process large amounts of data.

Data Strategies for Management and Organization -- (in Federal Systems): (4)

• Remember, implementing these strategies requires a collaborative effort across IT, security, and business stakeholders. Regular assessment and refinement of your data management practices are crucial for adapting to evolving needs and technologies.

Strategies for Data Management and Organization in Federal Systems: (4)

  1. Understanding Your Data:

1. Data Inventory: Start by creating a comprehensive inventory of all data across systems, devices, databases, and infrastructure. Include details like data type (structured, semi-structured, or unstructured), location, format, and sensitivity.

2. Data Classification: Classify data based on its sensitivity, criticality, legal requirements, and retention schedules. This helps prioritize protection and access controls.

  2. Structured Data:

1. Standardization: Implement data standards across systems to ensure consistency and facilitate easier integration and analysis. Define common data definitions, formats, and naming conventions.

2. Data Warehousing: Consider centralized data warehouses for consolidated storage and analysis of structured data from various sources.

3. Database Management: Implement sound database management practices like regular backups, disaster recovery plans, performance optimization, and access control.

  3. Unstructured Data:

1. Data Governance: Establish policies and procedures for handling unstructured data like emails, documents, and multimedia files. Implement retention schedules and disposal protocols.

2. Data Lake: Consider creating a data lake for flexible storage and management of diverse unstructured data.

3. Data Enrichment: Apply metadata tagging and text analytics to unlock the value of unstructured data for search, analysis, and insights.

  4. Backups and Virtual Environments:

1. Backup Strategy: Define a comprehensive backup strategy covering all data sources, including frequency, redundancy, and offsite storage. Regular backups are crucial for disaster recovery.

2. Virtual Environment Management: Implement policies and procedures for managing virtual environments, including access control, resource allocation, and disaster recovery plans.

Additional Considerations:

• Security: Implement appropriate security measures to protect data at rest, in transit, and in use. This includes encryption, access controls, and intrusion detection systems.

• Privacy: Ensure compliance with relevant data privacy regulations such as HIPAA and FERPA. Implement practices for data anonymization and pseudonymization where necessary.

• Scalability: Choose solutions that can scale to accommodate future data growth and evolving needs.

• Integration: Ensure your data management strategies allow for seamless integration with existing systems and applications.

• Training: Train staff on data security, privacy, and proper data management practices.

Resources:

• National Institute of Standards and Technology (NIST) Data Management Framework: https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1500-10r1.pdf

• Office of Management and Budget (OMB) Circular A-130: https://www.cio.gov/policies-and-priorities/circular-a-130/

• National Archives and Records Administration (NARA) Data Lifecycle Management: https://www.archives.gov/records-mgmt