Data Architecture

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What Is Data Architecture?

Data architecture maps the structure of an organization’s data and how the data flows to serve business objectives. Thus, quality data architecture helps companies leverage their data as a strategic asset, translate the data into an effective model and build their data strategy. Designing effective data architecture requires knowledge of both data needs and business processes.  

Data modeling and data strategy go hand-in-hand with data architecture. 

Data modeling works at the micro level. The models take care of the specific rules of certain data in a database and how to organize it, whereas data architecture addresses the macro level. The architecture offers a holistic perspective of all data in the business. Think of it like this: IKEA furniture often comes with an instruction brochure. The instructions correspond to the data modeling and tell you exactly how all the pieces fit together, while the furniture blueprint corresponds to the data architecture, which gives you a birds-eye view of the project. 

Meanwhile, data strategy relates to the data’s overall vision and framework. The strategy lays out guidance on data governance and policies, while the data architecture itself serves as the data strategy’s foundation. 

 

How Does Data Architecture Work?

In the past, simple data architecture has served organizations well. It used to be easy to map out a simple data architecture with a single database using extract, transform and load (ETL) processes. However, data architectures are becoming more complex. Advances in cloud computing, machine learning and data proliferation present new challenges. For example, real-time data analytics and pipelines increase the complexity of data architectures.  Companies now deal with such high-speed data that architectures must be able to handle spikes of data volumes when required. 

It’s important to remember that organizations must update data architectures as data and business needs shift. New technological breakthroughs might also demand revisions to data architecture.

More From Abdishakur HassanThe 7 Best Thematic Map Types for Geospatial Data

 

Why Is Data Architecture Important?

Organizations should have solid data architecture to support their business needs. Well-designed and updated data architectures enable organizations to:

  • Better understand data needs and align those needs with business requirements.
  • Develop sustainable and adaptable logical data structures to meet the organization’s future needs.
  • Aid in data management and governance.
  • Improve data quality and consistency.
  • Serve as a foundation for the company’s data strategies.
  • Help reduce data storage and processing costs by understanding the nature of data and its actual value.
Crash Course in Data Architecture | Video: Dataiku

 

Data Architecture Example

Data comes from different sources and also in various forms. Let’s take a look at a simple example of data architecture and its structure. 

graphic showing flow of data in data architecture system
Example data architecture flow. | Image: Abdishakur Hassan

The architecture should map out the sources of the data and formats (e.g., internal vs. external sources, structured vs. unstructured data). The next layer deals with data ingestion and storage. The data catalog binds together the organization’s data; the catalog matches the data source’s needs to the type of storage and processing the data requires. For example, a  data lake stores low-performant unstructured data while a data warehouse holds data in a structured format that can serve consumers. The analytics layer can aid in data processing with data science tools (see figure above). Finally, users should be able to access what they need, for example, data visualization and reports. 

 

Data Architecture Framework

Organizations can adapt standard architecture frameworks, which lay out the principles and standards to develop the data architecture roadmap.  One of these frameworks is The Open Group Architecture Framework (TOGAF). The TOGAF framework has a specific architecture development method (ADM) section, which describes how to develop and manage enterprise architecture. TOGAF also highlights architectural best practices. When organizations adopt architectural frameworks like the TOGAF, they can follow standard procedures and best practice guidelines to create high-quality data architectures that cater to their needs. 

 

What Are the Risks of Weak Data Architecture?

Rapid technological advances and changes in business needs can undermine effective data architectures. When we add new lines and shapes to a data architecture’s data flow,  it becomes very difficult to keep them updated and consistent. This can lead to duplicate data processes, high costs and increased maintenance time for business. Organizations should strive to balance the complexity of data architectures, business needs and the new tools/platforms for data management by carefully adopting new technologies that align with their vision and goals. 

Certifications

Data Architecture Certifications + Programs

General Assembly’s Data Science part-time course is a practical introduction to the interdisciplinary field of data science and machine learning, which lies at the intersection of computer science, statistics, and business. You will learn to use the Python programming language to acquire, parse, and model data for informing business strategy. 

This is a fast-paced course with some prerequisites. Students should be comfortable with programming fundamentals, core Python syntax, and basic statistics. There is an option to complete up to 25 hours of online preparatory lessons. Talk to the General Assembly Admissions team to discuss your background and confirm if this is the right fit for you..

 

What you'll accomplish

A significant portion of the course is a hands- on approach to fundamental modeling techniques and machine learning algorithms. You’ll also practice communicating your results and insights by compiling technical documentation and a stakeholder presentation. Throughout this expert-designed program, you’ll:

  • Perform exploratory data analysis with Python.
  • Build and refine machine learning models to predict patterns
  • from data sets.
  • Communicate data-driven insights to technical and non-technical audiences alike.
  • Apply what you’ve learned to create a portfolio project: a predictive model that addresses a real-world data problem.

 

Why General Assembly

Since 2011, General Assembly has graduated more than 40,000 students worldwide from the full time & part time courses. During the 2020 hiring shutdown, GA's students, instructors, and career coaches never lost focus, and the KPMG-validated numbers in their Outcomes report reflect it. *For students who graduated in 2020 — the peak of the pandemic — 74.4% of those who participated in GA's full-time Career Services program landed jobs within six months of graduation. General Assembly is proud of their grads + teams' relentless dedication and to see those numbers rising. Download the report here.

 

Your next step? Submit an application to talk to the General Assembly Admissions team


 

Note: reviews are referenced from Career Karma - https://careerkarma.com/schools/general-assembly

 

General Assembly

General Assembly’s Data Science Immersive is a transformative course designed for you to get the necessary skills for a data scientist role in three months. 

The Data Science bootcamp is led by instructors who are expert practitioners in their field, supported by career coaches that work with you since day one and enhanced by a career services team that is constantly in talks with employers about their tech hiring needs.

 

What you'll accomplish

As a graduate, you will be ready to succeed in a variety of data science and advanced analytics roles, creating predictive models that drive decision-making and strategy throughout organizations of all kinds. Throughout this expert-designed program, you’ll:

  • Collect, extract, query, clean, and aggregate data for analysis.
  • Gather, store and organize data using SQL and Git.
  • Perform visual and statistical analysis on data using Python and its associated libraries and tools.
  • Craft and share compelling narratives through data visualization.
  • Build and implement appropriate machine learning models and algorithms to evaluate data science problems spanning finance, public policy, and more.
  • Compile clear stakeholder reports to communicate the nuances of your analyses.
  • Apply question, modeling, and validation problem-solving processes to data sets from various industries to provide insight into real-world problems and solutions.
  • Prepare for the world of work, compiling a professional-grade portfolio of solo, group, and client projects.

 

Why General Assembly

Since 2011, General Assembly has graduated more than 40,000 students worldwide from the full time & part time courses. During the 2020 hiring shutdown, GA's students, instructors, and career coaches never lost focus, and the KPMG-validated numbers in their Outcomes report reflect it. *For students who graduated in 2020 — the peak of the pandemic — 74.4% of those who participated in GA's full-time Career Services program landed jobs within six months of graduation. General Assembly is proud of their grads + teams' relentless dedication and to see those numbers rising. Download the report here.

 

Your next step? Submit an application to talk to the General Assembly Admissions team


 

Note: reviews are referenced from Career Karma - https://careerkarma.com/schools/general-assembly

 

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