What are some of the skills and competencies that you need to use data architecture tools effectively?

Data literacy is key to designing an optimal data architecture. Understanding the business domain increases data literacy. The creation of quality curated data sets for users like business analysts, data analysts, data scientists, executives, managers, and other end users is possible through a thorough understanding of data usage, data access patterns, and data usability. Creation of metadata (data catalogs) for data discoverability even for non-tech savvy users is possible through data literacy.

It helps to understand the discussion in right context and convey your message in a manner, which is appropriate for target audience.

Data architects play a critical role in transforming raw data into meaningful information and actionable insights. They need to be able to analyze and interpret data in a way that helps stakeholders make informed decisions and solve business problems.

There are many good tools that Data Architects can use for Data Governance: Data catalogs: Data catalogs provide a central repository for information about data assets, such as their location, format, and usage. This can help to improve data discovery and make it easier to manage data quality. Data lineage tools: Data lineage tools track the flow of data through an organization's systems. This can help to identify data quality issues and improve data security. Data quality tools: Data quality tools help to identify and correct errors in data. This can improve data accuracy and make it easier to make informed decisions. Using these tools will help data architects to communicate data effectively to stakeholders.

While customising the legacy data model like IBM TDWM or Teradata FSLDM etc, standards must be followed, although it seems time consuming initially but juice worth the squeeze later on, otherwise it becomes cumbersome over time.

Here are some of the most common data modeling tools that an architect should know how to do: Entity-relationship (ER) modeling: ER modeling is a technique for representing data as entities and their relationships. Entities are objects that have attributes, relationships are the connections between entities. Dimensional modeling: Dimensional modeling is a technique for representing data as a set of dimensions and measures. Dimensions are the attributes of the data, measures are the values of the data. Graph-based modeling: Graph-based modeling is a technique for representing data as a graph, which is a collection of nodes and edges.

Bill Inmon has suggest to build an ODS (3NF) to solve data Integration related issues but if someone is intended to build data mart first, using bus matrix is the best way to integrate them afterwards.

Here are some of the most common data architecture patterns: Data hub: A data hub is a central repository for data from different sources. Data lake: A data lake is a storage repository for all types of data, both structured and unstructured. Data warehouse: A data warehouse is a centralized repository for data that is used for business intelligence and analytics. Data mart: A data mart is a smaller, more focused version of a data warehouse. It is typically used by a specific department or business unit. Data virtualization: Data virtualization is a technology that allows users to access data from different sources as if it were all stored in a single location.

Lakehouse architecture is a modern solution that integrates centralized governance strengths of data warehouses with the versatility of data lakes by utilizing tools like Delta Tables/Apache Iceberg/Apache Hudi, it ensures ACID compliance ath the file level, guaranteeing transactional integrity and data consistency. Metadata catalogs and data dictionaries further aid governance. For performance optimizations, Lakehouses use indexing, caching, and partitions to enhance data retrieval. It offers dual compatibility, supporting both data lake and warehouse APIs, which eases migration or integration of existing systems. As predominantly cloud-based solutions, they separate compute from storage, ensuring scalability and cost-efficiency.

It's important to start with the "why" when communicating about data. This is because people are more likely to be interested in and engaged with information if they understand the purpose of the data. When you start with the "why," you are giving your audience a reason to care about the data. You are also setting the stage for the rest of your communication, which will help to ensure that your message is clear and concise.