What is Microsoft Azure and what are its top alternatives?
Microsoft Azure is a cloud computing platform offered by Microsoft that provides a wide range of services including virtual machines, storage, databases, analytics, and more. Key features of Azure include scalability, flexibility, security, and a global network of data centers. However, some limitations of Azure include complex pricing structure, potential performance issues, and a steeper learning curve for beginners.
Amazon Web Services (AWS): AWS is one of the largest cloud computing platforms with a wide range of services, strong global presence, and flexible pricing options. Pros include a vast service catalog, strong security, and global reach. Cons include complex pricing and a steep learning curve.
Google Cloud Platform (GCP): GCP offers services for computing, storage, machine learning, and more with strong focus on data analytics and AI. Pros include strong machine learning capabilities, flexible pricing, and global network. Cons include less services compared to AWS and Azure.
IBM Cloud: IBM Cloud offers a range of cloud services including AI, blockchain, and Internet of Things (IoT) with a focus on enterprise solutions. Pros include strong security features, compliance-ready services, and hybrid cloud options. Cons include limited third-party services compared to AWS and Azure.
Oracle Cloud Infrastructure (OCI): OCI provides compute, storage, and networking services along with database options with a focus on enterprise-grade performance and security. Pros include high performance computing options, strong database offerings, and hybrid cloud support. Cons include limited third-party integrations and smaller service catalog compared to AWS and Azure.
Alibaba Cloud: Alibaba Cloud is a leading cloud provider in China offering a wide range of services including data storage, databases, and AI. Pros include strong presence in Asia, flexible pricing options, and global network. Cons include limited global reach compared to other cloud providers.
DigitalOcean: DigitalOcean provides simple cloud infrastructure with services for virtual machines, databases, and Kubernetes. Pros include ease of use, predictable pricing, and strong community support. Cons include limited services compared to AWS and Azure.
VMware Cloud: VMware Cloud offers a range of cloud services for virtualization, networking, and management with a focus on hybrid cloud solutions. Pros include seamless integration with existing VMware environments, enterprise-grade security, and multi-cloud management. Cons include limited support for non-VMware workloads and higher costs for some services.
Red Hat OpenShift: OpenShift is a container platform based on Kubernetes offering container orchestration, automation, and developer tools. Pros include easy container deployment, strong security features, and extensive ecosystem of tools. Cons include complex setup for beginners and potential scalability challenges.
Salesforce Heroku: Heroku is a cloud platform that enables developers to build, deliver, monitor, and scale applications quickly and efficiently. Pros include ease of use, streamlined development process, and strong support for multiple programming languages. Cons include limited scalability options for large applications.
Hewlett Packard Enterprise (HPE) GreenLake: HPE GreenLake offers a range of cloud services including infrastructure, data storage, and workload management with a focus on pay-per-use pricing model. Pros include flexible consumption options, strong security features, and simplified IT operations. Cons include limited service catalog compared to major cloud providers.
Top Alternatives to Microsoft Azure
- Google Cloud Platform
It helps you build what's next with secure infrastructure, developer tools, APIs, data analytics and machine learning. It is a suite of cloud computing services that runs on the same infrastructure that Google uses internally for its end-user products, such as Google Search and YouTube. ...
- DigitalOcean
We take the complexities out of cloud hosting by offering blazing fast, on-demand SSD cloud servers, straightforward pricing, a simple API, and an easy-to-use control panel. ...
- OneDrive
Outlook.com is a free, personal email service from Microsoft. Keep your inbox clutter-free with powerful organizational tools, and collaborate easily with OneDrive and Office Online integration. ...
- Hadoop
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. ...
- Oracle
Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database. ...
- NGINX
nginx [engine x] is an HTTP and reverse proxy server, as well as a mail proxy server, written by Igor Sysoev. According to Netcraft nginx served or proxied 30.46% of the top million busiest sites in Jan 2018. ...
- Apache HTTP Server
The Apache HTTP Server is a powerful and flexible HTTP/1.1 compliant web server. Originally designed as a replacement for the NCSA HTTP Server, it has grown to be the most popular web server on the Internet. ...
- Amazon EC2
It is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale computing easier for developers. ...
Microsoft Azure alternatives & related posts
- Good app Marketplace for Beginner and Advanced User5
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- Premium tier IP address3
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- Cheap3
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I want to make application like Zomato, #Foodpanda.
Which stack is best for this? As I have expertise in Java and Angular. What is the best stack you will recommend?
Web Micro-service / Mono? Angular / React? Amazon Web Services (AWS) / Google Cloud Platform? DB : SQL or No SQL
Mob Cross-platform: React Native / Flutter
Note: We are a team of 5. what languages do you recommend if I go with microservices?
Thanks
My days of using Firebase are over! I want to move to something scalable and possibly less cheap. In the past seven days I have done my research on what type of DB best fits my needs, and have chosen to go with the nonrelational DB; MongoDB. Although I understand it, I need help understanding how to set up the architecture. I have the client app (Flutter/ Dart) that would make HTTP requests to the web server (node/express), and from there the webserver would query data from MongoDB.
How should I go about hosting the web server and MongoDb; do they have to be hosted together (this is where a lot of my confusion is)? Based on the research I've done, it seems like the standard practice would be to host on a VM provided by services such as Amazon Web Services, Google Cloud Platform, Microsoft Azure, etc. If there are better ways, such as possibly self-hosting (more responsibility), should I? Anyways, I just want to confirm with a community (you guys) to make sure I do this right, all input is highly appreciated.
DigitalOcean
- Great value for money560
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- Nice ui250
- Easy configuration191
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- Ssh access138
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- Ubuntu24
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- Variety of services0
- Managed Kubernetes0
- No live support chat3
- Pricing3
related DigitalOcean posts
This week, we finally released NurseryPeople.com. In the end, I chose to provision our server on DigitalOcean. So far, I am SO happy with that decision. Although setting everything up was a challenge, and I learned a lot, DigitalOceans blogs helped in so many ways. I was able to set up nginx and the Laravel web app pretty smoothly. I am also using Buddy for deploying changes made in git, which is super awesome. All I have to do in order to deploy is push my code to my private repo, and buddy transfers everything over to DigitalOcean. So far, we haven't had any downtime and DigitalOceans prices are quite fair for the power under the hood.
Hello, I'm currently writing an e-commerce website with Laravel and Laravel Nova (as an admin panel). I want to start deploying the app and created a DigitalOcean account. After some searches about the deployment process, I saw that the setup via DigitalOcean (using Droplets) isn't very easy for beginners. Now I'm not sure how to deploy my app. I am in between Laravel Forge and DigitalOcean (?Apps Platform or Droplets?). I've read that Heroku and Laravel Vapor are a bit expensive. That's why I didn't consider them yet. I'd be happy to read your opinions on that topic!
- FREE2
- Simple2
- Back up1
- Stable service1
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- Great ecosystem39
- One stack to rule them all11
- Great load balancer4
- Amazon aws1
- Java syntax1
related Hadoop posts
The early data ingestion pipeline at Pinterest used Kafka as the central message transporter, with the app servers writing messages directly to Kafka, which then uploaded log files to S3.
For databases, a custom Hadoop streamer pulled database data and wrote it to S3.
Challenges cited for this infrastructure included high operational overhead, as well as potential data loss occurring when Kafka broker outages led to an overflow of in-memory message buffering.
Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :
Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:
https://eng.uber.com/marmaray-hadoop-ingestion-open-source/
(Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )
Oracle
- Reliable44
- Enterprise33
- High Availability15
- Hard to maintain5
- Expensive5
- Maintainable4
- Hard to use4
- High complexity3
- Expensive14
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Hi. We are planning to develop web, desktop, and mobile app for procurement, logistics, and contracts. Procure to Pay and Source to pay, spend management, supplier management, catalog management. ( similar to SAP Ariba, gap.com, coupa.com, ivalua.com vroozi.com, procurify.com
We got stuck when deciding which technology stack is good for the future. We look forward to your kind guidance that will help us.
We want to integrate with multiple databases with seamless bidirectional integration. What APIs and middleware available are best to achieve this? SAP HANA, Oracle, MySQL, MongoDB...
ASP.NET / Node.js / Laravel. ......?
Please guide us
I recently started a new position as a data scientist at an E-commerce company. The company is founded about 4-5 years ago and is new to many data-related areas. Specifically, I'm their first data science employee. So I have to take care of both data analysis tasks as well as bringing new technologies to the company.
They have used Elasticsearch (and Kibana) to have reporting dashboards on their daily purchases and users interactions on their e-commerce website.
They also use the Oracle database system to keep records of their daily turnovers and lists of their current products, clients, and sellers lists.
They use Data-Warehouse with cockpit 10 for generating reports on different aspects of their business including number 2 in this list.
At the moment, I grab batches of data from their system to perform predictive analytics from data science perspectives. In some cases, I use a static form of data such as monthly turnover, client values, and high-demand products, and run my predictive analysis using Python (VS code). Also, I use Google Datastudio or Google Sheets to present my findings. In other cases, I try to do time-series analysis using offline batches of data extracted from Elastic Search to do user recommendations and user personalization.
I really want to use modern data science tools such as Apache Spark, Google BigQuery, AWS, Azure, or others where they really fit. I think these tools can improve my performance as a data scientist and can provide more continuous analytics of their business interactions. But honestly, I'm not sure where each tool is needed and what part of their system should be replaced by or combined with the current state of technology to improve productivity from the above perspectives.
NGINX
- High-performance http server1.4K
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- Open source607
- Load balancer530
- Free289
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- Web server226
- Simplicity175
- Easy setup136
- Content caching30
- Web Accelerator21
- Capability15
- Fast14
- High-latency12
- Predictability12
- Reverse Proxy8
- Supports http/27
- The best of them7
- Great Community5
- Lots of Modules5
- Enterprise version5
- High perfomance proxy server4
- Embedded Lua scripting3
- Streaming media delivery3
- Streaming media3
- Reversy Proxy3
- Blash2
- GRPC-Web2
- Lightweight2
- Fast and easy to set up2
- Slim2
- saltstack2
- Virtual hosting1
- Narrow focus. Easy to configure. Fast1
- Along with Redis Cache its the Most superior1
- Ingress controller1
- Advanced features require subscription10
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Our whole DevOps stack consists of the following tools:
- GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
- Respectively Git as revision control system
- SourceTree as Git GUI
- Visual Studio Code as IDE
- CircleCI for continuous integration (automatize development process)
- Prettier / TSLint / ESLint as code linter
- SonarQube as quality gate
- Docker as container management (incl. Docker Compose for multi-container application management)
- VirtualBox for operating system simulation tests
- Kubernetes as cluster management for docker containers
- Heroku for deploying in test environments
- nginx as web server (preferably used as facade server in production environment)
- SSLMate (using OpenSSL) for certificate management
- Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
- PostgreSQL as preferred database system
- Redis as preferred in-memory database/store (great for caching)
The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:
- Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
- Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
- Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
- Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
- Scalability: All-in-one framework for distributed systems.
- Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
We chose AWS because, at the time, it was really the only cloud provider to choose from.
We tend to use their basic building blocks (EC2, ELB, Amazon S3, Amazon RDS) rather than vendor specific components like databases and queuing. We deliberately decided to do this to ensure we could provide multi-cloud support or potentially move to another cloud provider if the offering was better for our customers.
We’ve utilized c3.large nodes for both the Node.js deployment and then for the .NET Core deployment. Both sit as backends behind an nginx instance and are managed using scaling groups in Amazon EC2 sitting behind a standard AWS Elastic Load Balancing (ELB).
While we’re satisfied with AWS, we do review our decision each year and have looked at Azure and Google Cloud offerings.
#CloudHosting #WebServers #CloudStorage #LoadBalancerReverseProxy
Apache HTTP Server
- Web server479
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- Fast148
- Ssl support138
- Since 199644
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- Proven over many years4
- Mature2
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- Perfect Support1
- Many available modules0
- Many available modules0
- Hard to set up4
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When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?
So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.
React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.
Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.
We've been happy with nginx as part of our stack. As an open source web application that folks install on-premise, the configuration system for the webserver is pretty important to us. I have a few complaints (e.g. the configuration syntax for conditionals is a pain), but overall we've found it pretty easy to build a configurable set of options (see link) for how to run Zulip on nginx, both directly and with a remote reverse proxy in front of it, with a minimum of code duplication.
Certainly I've been a lot happier with it than I was working with Apache HTTP Server in past projects.
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- Free tier67
- Easy management, scalability58
- Flexible13
- Easy to Start10
- Widely used9
- Web-scale9
- Elastic9
- Node.js API7
- Industry Standard5
- Lots of configuration options4
- GPU instances2
- Simpler to understand and learn1
- Extremely simple to use1
- Amazing for individuals1
- All the Open Source CLI tools you could want.1
- Ui could use a lot of work13
- High learning curve when compared to PaaS6
- Extremely poor CPU performance3
related Amazon EC2 posts
To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.
Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.
We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.
Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.
Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.
#BigData #AWS #DataScience #DataEngineering
Our whole DevOps stack consists of the following tools:
- GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
- Respectively Git as revision control system
- SourceTree as Git GUI
- Visual Studio Code as IDE
- CircleCI for continuous integration (automatize development process)
- Prettier / TSLint / ESLint as code linter
- SonarQube as quality gate
- Docker as container management (incl. Docker Compose for multi-container application management)
- VirtualBox for operating system simulation tests
- Kubernetes as cluster management for docker containers
- Heroku for deploying in test environments
- nginx as web server (preferably used as facade server in production environment)
- SSLMate (using OpenSSL) for certificate management
- Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
- PostgreSQL as preferred database system
- Redis as preferred in-memory database/store (great for caching)
The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:
- Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
- Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
- Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
- Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
- Scalability: All-in-one framework for distributed systems.
- Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.