From Ingestion to Visualisation: Choosing the Public Cloud Provider for Your Data Journey
Introduction:
In the rapidly evolving landscape of cloud computing, selecting the right cloud provider is crucial for effectively managing the entire data life cycle. From ingestion to visualization, each stage of the data life cycle requires robust and reliable services. This blog will comprehensively evaluate major cloud providers based on their capabilities across the key stages of the data life cycle: ingestion, storage, processing and analysis, exploration, and visualization.
Objectives:
To help data professionals and businesses make informed decisions about cloud providers.
To highlight the strengths and weaknesses of each cloud provider in different stages of the data life cycle.
To provide actionable insights for optimising data workflows and achieving data-driven success.
Target Audience:
Data engineers and architects
Data analysts and scientists
Business intelligence professionals
IT decision-makers and cloud architects
Let us begin exploring data and how Automobile Manufacturing uses it.
Let us quickly categorise the above data:
It's important to recognise that the categorisation of certain data types can vary based on how the IT sector, particularly in the automotive industry, collects, processes, and stores information. For example, sensor data may be classified as structured if it is immediately processed and stored in a relational database, while it may be considered semi-structured under different circumstances.
Before we get to the Data life cycle, let us take a quick overview of the cloud providers.
Cloud Providers Overview
For simplicity, I’ll focus on the market leaders, AWS, Azure, and Google Cloud. I'll also include highlights of their key services and innovation trends.
Summary Comparison
Key Trends
AI/ML Explosion (2020s):
All providers now prioritize generative AI, with AWS (Bedrock), Azure (OpenAI), and GCP (Vertex AI) offering LLM-based tools.
Serverless Dominance:
AWS Lambda, Azure Functions, and Google Cloud Functions drive event-driven architectures.
Unified Analytics:
Azure Synapse, AWS Glue, and Google BigQuery unify data lakes, warehouses, and ML.
Hybrid/Multi-Cloud:
Azure Arc, AWS Outposts, and Google Anthos bridge on-premises and cloud.
Introduction to the Data Life Cycle
A brief overview of the data life cycle stages: ingestion, storage, processing and analysis, exploration, and visualisation.
Data Ingestion
These services cater to different aspects of data ingestion, from real-time streaming to batch processing, and each has its own strengths depending on your specific use case and cloud environment. In our automobile industry example - this would be the layer where we
Key considerations: pre-build connectors, automated schema migrations, data sources, ingestion tools, and performance.
Here is a summary table:
Data Storage
These services are widely used for storing structured, semi-structured, and unstructured data, and they cater to various use cases like data lakes, analytics, backups, and more.
Key considerations: scalability(eg, auto-scaling), durability, and cost (Tiered options based on the frequency of the data being accessed)
Data Processing and Analysis
Key considerations
Processing power: We must simplify resource management for processing any volume of data. In terms of scalable clusters & serverless options.
Analytical tools: The simple SQL-based tools are efficient in terms of a small set of data. The spark-based tools are better for big-data and ML workflows.
AI & Machine learning integration: Most cloud providers support end-to-end ML capabilities with their analytical platforms & support AI.
Data Exploration
Let’s explore data exploration tools across cloud providers, focusing on ease of use, analytical depth, integration with BI tools, insights generation, and AI/ML capabilities. Below is a breakdown of the services you mentioned, along with additional options organised by the cloud provider.
Data Visualisation
Deriving insights from the data & empowering teams based on the data to tell an impactful story - visualisation is the key. A comprehensive overview of data visualization tools, including cloud-based, on-premises, and hybrid solutions, evaluated based on available visuals, customisation, integration with AI (e.g., natural language interaction, automated insights), and their ability to support human-like conversational analytics.
Things to consider for implementation
Define the business objective and targeted outcome
Target quick and long term wins
With the cloud and the services available on it, there are possibilities to do a quick experimentation, Fit for use - Minimum Viable Product
Storage & Computation power are friends here 😊 need to strategically utilise it to unleash the power of data
The above list of services on the cloud is for reference. I have not provided in-depth or up-to-date details covering the services. This can accelerate your technical discovery process.
Conclusion:
This blog will serve as a valuable resource for anyone looking to understand the strengths and limitations of major cloud providers in the context of the data life cycle. By providing a detailed evaluation, we aim to help readers make informed decisions that align with their business objectives and technical requirements.