Enter the world of Quantum Computing with a 3-part Introduction
Quantum computing is poised to change the computing landscape, unleashing solutions for a plethora of super complex, hitherto unsolvable or time-demanding problems. Google’s demonstration of quantum supremacy last year was just one of the steps in this direction. There are multiple startups, multi-nationals and government led projects which are already making major strides towards a quantum-powered world.
As more research gets done in this area, it’s only a matter of time but yes, it will take some time, until we see use of quantum computing for desired applications. This series of articles aims to introduce you to the quantum world, explain why there is a lot of excitement in this space, what are the different ways to build a quantum computer, what are its limits, who are the current big players, how you can get started with quantum programming etc. …
All the important features of a serverless, modern multi-cloud data warehouse with best practices in one go!
BigQuery is a petabyte-scale, serverless, highly scalable, and cost-effective, multi-cloud data warehouse designed for business agility. Compared to BigQuery, traditional data warehouses have the following problems:
- It’s very hard to scale compute and storage.
- Upgrades require a lot of planning (months!) and downtime.
- It is hard to keep up with data growth.
- It has rather complex ETL.
- It doesn’t support Machine Learning or AI, imperative for the modern Business Intelligence.
- It is restricted to only a few users because of costly licenses.
- It needs continuous patching and updates and requires a large administration team for operations.
Using BigQuery, you can automate delivery of the data, simplify data operations, use machine learning and AI etc. There is no need to do capacity planning on your side or worry about licensing. You can easily make the insights available for others, including real-time insights. It also provides all customers with 10 GB storage and up to 1 TB queries/month, completely free of charge. …
The whitepaper on AWS Serverless Application Lens is a treasure trove of information on how to correctly design a serverless application on AWS. However, if you are short on time and want a gist of the whitepaper, this article aims to provide that for you. Although in order to become an AWS serverless ninja, the whitepaper, especially the links included in it are highly recommended.
Overview
Following is an overview of all the services that are generally included in a serverless architecture. It is assumed that the reader knows the general functionality of each of these services.
Design Principles
An Azure Fundamentals summary — a start to your Azure journey or for exam preparation
Important: This article is a 20-minute long read, but what should make it worth your while is the fact that it introduces a lot of important Azure / Cloud concepts for starters. So I hope you enjoy it, let’s begin!
Overview
Let’s start with the reason why any cloud is a cheaper option than others: Economies of Scale. Economy of scale is the ability to reduce costs and gain efficiency when operating at a larger scale in comparison to operating at a smaller scale. …
A few new exciting features for Compute, Storage, Database etc. on AWS
This article elucidates some of the new features made available recently / coming soon to AWS. Each feature is categorized under the product type — so let’s get started!
Compute
Container support and microsecond billing in Lambda
You can now use your existing container development workflows to build lambda applications by packaging and deploying Lambda functions as container images of up to 10 GB in size. This features allows you to use the same set of tools for containers and lambda based applications. …
Understand the most prominent cloud service models
IaaS, CaaS, PaaS, FaaS and SaaS are all different categories of cloud computing services. Cloud computing services help you offload/outsource management of your networking, storage, HVAC, Virtualization, Runtimes, Container tech etc. You can thus reduce your CapEx costs considerably while relatively increasing your OpEx costs. This helps you reduce your IT costs in the long run while increasing your agility, developer velocity and improving your Time-to-Market.
In the following sections, we will go through the different categories while providing their respective use cases, examples and payment structures.
IaaS — Infrastructure-as-a-Service
There are multiple ways to look at and explain IaaS. Here we will look at the two most prominent ones: business model and online services. …
Use a unified programming model for both batch and streaming use cases — and run in a serverless fashion on Google Cloud
Dataflow is one of the runners for the open source Apache Beam framework. Apache Beam is an open source, unified model for defining both batch- and streaming-data parallel-processing pipelines. It allows you to execute your pipelines on multiple execution environments like Dataflow, Spark, Samza, Flink etc. The following diagram shows an example stream analytics pipeline possible on Google cloud using Dataflow.
Before we get into Dataflow / Apache Beam, let’s first check out the situations for which Dataflow is a good fit.
Dataflow vs. Dataproc
Dataproc and Dataflow are the two data processing option available on Google cloud. Both allow you to define batch- and streaming-data parallel-processing pipelines. …
Get acquainted to all the necessary tools in your governance journey on Azure
Governance in the cloud is essential to ensure compliance, have an administrative control over what’s deployed in the cloud while making sure that the costs don’t go out of hand. The compliance dictates where you are allowed to run your VMs, store your data, how authentication and authorization are performed in the cloud etc. This article aims to introduce some of the primary services used on Azure to assist you in your governance journey and points out some important features.
Azure Policy
Azure Policy helps you to enforce your organizational standards and to assess compliance at-scale. It also helps to bring your resources to compliance through bulk remediation for existing resources and automatic remediation for new resources. Common use cases for Azure Policy include implementing governance for resource consistency, regulatory compliance, security, cost, and management. …
It’s important to follow best practices when starting out a new project, regardless of whether it’s on cloud or not. This article introduces some of these best practices / recommendations for Azure that might be helpful for you in your project. So let’s get started!
Don’t reinvent the wheel
Azure has a lot of existing resources, i.e. Azure Resource Manager templates, azure policies, sample codes etc. that you can employ as-is in your projects.
- Resource Manager Templates — A repository of resource manager templates.
- Serverless — A repository of serverless examples including technologies like Logic Apps, Azure Functions etc.
- Azure Samples — Code samples and examples in .NET, Java, Python, Node.js, …
CAP Theorem, ACID, BASE, ETL, ELT, OTLP, OLAP and lots more — all in one go
This is the second part of a multi-part introduction to data. You can read the first part for an introduction to data types, data processing options, CAP Theorem, ACID, BASE etc. here. This part will introduce more concepts so let’s get started.
Key concepts across Databases / Data Warehouses
ETL vs. ELT vs. EL
Data Analytics usually involves three stages
The data processing stage occurs after the data has been ingested and collected. Data processing takes the data in its raw form, cleans it, and converts it into a more meaningful format. …
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