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Here's how you can acquire the essential technical skills for entry level Data Architects.

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Embarking on a career as a Data Architect can be both exciting and daunting. You're diving into a world where you'll be responsible for designing, creating, managing, and optimizing data systems, which are the backbone of modern businesses. The good news is that acquiring the essential technical skills for an entry-level position is attainable with the right approach and dedication. Whether you're a recent graduate or looking to shift careers, understanding the fundamentals of data architecture and the technologies involved is your first step toward a successful career in this field.

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Axel Schwanke

Senior Data Engineer | Data Architect | Data Science | Data Mesh | Data Governance | 4x Databricks certified | 2x AWS…
Gandhi Buddha

Staff Engineer | Spark | SQL | Python | GCP | Databricks | Kafka | Writes to 6k+

1 Learn SQL

Structured Query Language (SQL) is the cornerstone of data manipulation and retrieval. As an aspiring Data Architect, you must be proficient in SQL as it allows you to interact with databases. Start by learning the basics of creating, reading, updating, and deleting data (CRUD operations). Then, advance to more complex queries involving joins, subqueries, and views. Practicing with real-world scenarios will help solidify your understanding and prepare you for designing efficient database structures.

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Gandhi Buddha

Staff Engineer | Spark | SQL | Python | GCP | Databricks | Kafka | Writes to 6k+
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Being a data architect is neither easy nor tough. SQL comes first being a data guy to become a data architect So, one should learn not only the concepts but also the internals and performance tuning of sql as an entry level data architect ??

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2 Master Data Modeling

Data modeling is a method used to define and analyze data requirements needed to support the business processes within information systems. As a foundational skill for Data Architects, you need to understand how to create conceptual, logical, and physical data models. Begin with conceptual modeling to establish the scope and then progress to logical models that define the structure of the data without being tied to any database technology. Finally, learn physical data modeling to translate your logical models into actual database schemas.

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Chetan Sharma

SAP Architect | TOGAF? EA Practitioner | PMP ?
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Data architects need an in-depth knowledge of their industry. This includes the trends and best practices regarding data collection, storage, and analysis. Structured Query Language (SQL) is an essential and fundamental language for data architects. Data visualization involves presenting data in graphical styles to express insights from data which are the fundamental skills for a Data Architect. Problem-solving is a necessary soft skill for data architects. Data architect requires efficient communication skills to successfully communicate plans and instructions to team members. Data modeling is another essential skill for a data architect. It involves representing the manner of data flow through simple diagrams of a complex software system.

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Gandhi Buddha

Staff Engineer | Spark | SQL | Python | GCP | Databricks | Kafka | Writes to 6k+
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Handling data comes with lot of columns and tables and records so one should have great understanding of data modelling and be aware of how to do as well

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3 Understand ETL

Extract, Transform, Load (ETL) processes are vital in moving data from various sources into a data warehouse. To develop this skill, familiarize yourself with the concepts of data extraction from heterogeneous sources, data transformation to fit operational needs, and data loading into the target database. Knowledge of ETL tools and the ability to automate these processes will be invaluable in ensuring that data is accurate and available for analysis.

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Gandhi Buddha

Staff Engineer | Spark | SQL | Python | GCP | Databricks | Kafka | Writes to 6k+
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Going back to roots of data transformation data architect should not forget ETL in deep as it’s the essence in Data Engineering so in data architecture

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4 Grasp Big Data

Big Data refers to large, complex data sets that traditional data processing software can't handle. As data volumes continue to explode, understanding Big Data technologies and frameworks like Hadoop or Spark is essential. Start by grasping the basics of distributed storage and processing. Learn how to work with unstructured data and gain experience with Big Data querying languages such as Hive or Pig.

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5 Explore Cloud Services

Cloud services have become a significant component of modern data architecture. They offer scalable and flexible resources for storing and processing data. Acquaint yourself with cloud computing basics and the services provided by major cloud platforms like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP). Learn about cloud-based data warehousing solutions and how to architect data storage and compute resources in the cloud.

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Gandhi Buddha

Staff Engineer | Spark | SQL | Python | GCP | Databricks | Kafka | Writes to 6k+
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Being a data architect you are expected to know cloud services as we are migrating all the on prem data and applications to cloud so as a beginner data architect you should learn any cloud and if you have time you should learn multiple clouds as you can suggest the better cloud for clients.

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6 Practice Design Principles

Finally, understanding design principles is crucial for creating efficient and scalable data architectures. Study best practices for data normalization to reduce redundancy, and learn about denormalization for performance optimization. Explore different database types such as relational, NoSQL, and NewSQL, and understand when each is appropriate. Practice designing data systems that are resilient, secure, and able to handle growth.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Axel Schwanke

Senior Data Engineer | Data Architect | Data Science | Data Mesh | Data Governance | 4x Databricks certified | 2x AWS certified | 1x CDMP certified | Medium Writer | Nuremberg, Germany
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To excel as an entry-level data architect, you should also ... Data governance: Understanding the principles of data governance, including data quality, security and compliance (e.g. GDPR, EU AI Act), is essential to ensure that the data architecture you design meets regulatory requirements and protects sensitive information. Emerging technologies: Keep up to date with the latest tools and technologies in data architecture such as semantic layers. This includes understanding modern data platforms and artificial intelligence (AI) integration that can greatly enhance data processing and analytics capabilities.

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Brett Goodlad

??7 x LinkedIn Top Voice | Experienced Full-Stack Expert | Delivering digital solutions from Front to Back End | Leveraging Performance, Efficiency & Improvement
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Start by mastering foundational concepts such as database management systems (SQL, NoSQL) and data modelling techniques. Gain proficiency in data warehousing principles and understand ETL (Extract, Transform, Load) processes. Familiarize yourself with cloud platforms like AWS, Azure, or Google Cloud, learning how to deploy and manage databases in these environments. Develop expertise in data integration methodologies and tools like Apache Kafka or Informatica for seamless data flow.

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Here's how you can acquire the essential technical skills for entry level Data Architects.

1

2

3

4

5

6

7

1 Learn SQL

2 Master Data Modeling

3 Understand ETL

4 Grasp Big Data

5 Explore Cloud Services

6 Practice Design Principles

7 Here’s what else to consider

Data Architecture

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Here's how you can acquire the essential technical skills for entry level Data Architects.

1

2

3

4

5

6

7

1 Learn SQL

2 Master Data Modeling

3 Understand ETL

4 Grasp Big Data

5 Explore Cloud Services

6 Practice Design Principles

7 Here’s what else to consider

Data Architecture

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