Introduction
Welcome to Data Accelerators technical series on data analytics architecture!
Why now?
So many times colleagues and stakeholders asked me to share with them what I learned during the years. Hand over the knowledge and experience of building various data systems. Finally I got to do so.
In this series. we will introduce you to the basics of how data analytics systems work and the various components that make them up. We will start from the ground up, explaining concepts such as data storage, data processing, and data visualization in simple terms. We will also cover some of the key technologies and best practices used in the field of data analytics architecture. Whether you are new to data analytics or just looking to brush up on the basics, this series has something for you. Join us as we explore the exciting world of data analytics architecture!
Data Architecture Components
Data analytics architecture refers to the infrastructure and tools used to collect, store, process, and analyze data for the purpose of deriving insights and making data-driven decisions. It typically involves a number of different components, including:
- Data sources: These are the systems or devices that generate data, such as sensors, databases, or application logs.
- Data Acquisition: Data acquisition refers to the process of collecting data for analysis. It can be done in real-time (streaming) or in periodic batches (batch), depending on the specific needs and constraints of the data analytics system. Both approaches have their own advantages and are used in different scenarios.
- Data processing: This is the component responsible for transforming raw data into a form that is suitable for analysis. This may involve tasks such as filtering, aggregation, or transformation of the data.
- Data storage: This is where data is persisted, typically in a database or data warehouse. The choice of data storage technology depends on the type and volume of data being collected, as well as the performance and scalability requirements of the analytics system.
- Data governance: This refers to the policies and processes in place to ensure that data is collected, stored, and used in a responsible and ethical manner. This may include issues such as data privacy, security, and compliance.
- Data Products: This is the component that allows users to interact with and explore the data, typically through dashboards or charts.
In this technical series, we will delve into these and other components of data analytics architecture, exploring the various technologies and best practices for building and maintaining a robust and effective system for data-driven decision making.
Why We Need Data Architecture in the World of AI and Machine Learning
Data Analytics architecture is the foundation upon which data analysis and advanced analytics are built. It provides the infrastructure and tools for collecting, storing, and processing data, as well as defining the relationships and structures within the data. To connect data architecture to data analysis and advanced analytics, you need to ensure that the data is properly cleaned, transformed, and integrated into the analytics system. This may involve tasks such as data wrangling, data integration, and data modeling. With a well-designed data architecture in place, you can then use data analysis and advanced analytics techniques such as machine learning and statistical modeling to extract insights and make data-driven decisions.
How Cloud Technologies Impacted Data Analytics Architecture
Cloud technologies have had a significant impact on data analytics architecture in recent years. Some of the ways that cloud technologies have influenced this field include:
- Increased scalability and flexibility: Cloud-based data analytics architectures can scale up or down on demand, making it easier to handle fluctuating workloads and data volumes.
- Improved cost-efficiency: Cloud-based data analytics architectures can reduce the cost of hardware and maintenance, as well as allowing for a pay-as-you-go pricing model.
- Enhanced security and compliance: Many cloud providers offer robust security and compliance features, which can be especially beneficial for regulated industries.
- Greater collaboration and sharing: Cloud-based data analytics architectures can facilitate collaboration and sharing of data and insights among team members, partners, and customers.
- Streamlined development and deployment: Cloud technologies can make it easier to develop and deploy data analytics applications, thanks to features such as automated provisioning and continuous delivery.
In this series we will demonstrate cloud first approach and will provide Data Architecture Design in the cloud eco-system.
How to Start From Zero or Within Existing Analytics System
- Define your business needs and objectives: What do you want to achieve with your data analytics system, and what data do you need to support those goals?
- Identify your data sources: Where does your data come from, and what format and structure does it have?
- Design your data analytics architecture: Based on your business needs and data sources, design a logical and physical data analytics architecture that includes components such as data storage, data processing, and data visualization. Consider factors such as scalability, performance, and data governance as you design your system.
What Next…
In the next section of our data analytics architecture series, we will dive into the topic of data sources. We will cover the various types of data sources that are commonly used in data analytics systems, including structured and unstructured data sources. We will also discuss how to integrate data from multiple sources and how to manage the quality and consistency of the data. Stay tuned for our next blog on data sources, the first part in the data analytics architecture series!
