Sean  Doyle

Sean Doyle

1624930125

Recommendation Engine and Azure ML Series - Introduction to Azure ML

This session will be an introduction to AzureML and we will introduce the concepts of Azure ML by building a model using Azure ML and deploying it in Azure Kubernetes Services. This session will focus on Azure ML - Training and Deploying a Model into Azure Kubernetes Service from scratch.

Speaker BIO- Ambarish is a Business and Technology Consultant for more than 20 Years. He is a data lover and compete regularly in data science competitions. He had won Eight Data Science Competition Awards ( 1 sponsored by NASA, Seven sponsored by Kaggle - A Google Company). He presently works with Tata Consultancy Services (TCS) as the Energy Trading and Risk Management Lead, as well as Data Analytics Practice Lead for TCS Utilities. This is a very exciting position where he has the opportunity to combine domain knowledge with data. He is very fortunate to be surrounded by very enthusiastic and energetic people. He do follow cricket, loves to read detective books, and also watch detective films.
Social Handles-
LinkedIn- https://www.linkedin.com/in/ambarish-ganguly/
Twitter- https://twitter.com/a_ganguly/

#machine-learning #azure

What is GEEK

Buddha Community

Recommendation Engine and Azure ML Series - Introduction to Azure ML
Aisu  Joesph

Aisu Joesph

1626490533

Azure Series #2: Single Server Deployment (Output)

No organization that is on the growth path or intending to have a more customer base and new entry into the market will restrict its infrastructure and design for one Database option. There are two levels of Database selection

  • a.  **The needs assessment **
  • **b. Selecting the kind of database **
  • c. Selection of Queues for communication
  • d. Selecting the technology player

Options to choose from:

  1. Transactional Databases:
    • Azure selection — Data Factory, Redis, CosmosDB, Azure SQL, Postgres SQL, MySQL, MariaDB, SQL Database, Maria DB, Managed Server
  2. Data warehousing:
    • Azure selection — CosmosDB
    • Delta Lake — Data Brick’s Lakehouse Architecture.
  3. Non-Relational Database:
  4. _- _Azure selection — CosmosDB
  5. Data Lake:
    • Azure Data Lake
    • Delta Lake — Data Bricks.
  6. Big Data and Analytics:
    • Data Bricks
    • Azure — HDInsights, Azure Synapse Analytics, Event Hubs, Data Lake Storage gen1, Azure Data Explorer Clusters, Data Factories, Azure Data Bricks, Analytics Services, Stream Analytics, Website UI, Cognitive Search, PowerBI, Queries, Reports.
  7. Machine Learning:
    • Azure — Azure Synapse Analytics, Machine Learning, Genomics accounts, Bot Services, Machine Learning Studio, Cognitive Services, Bonsai.

Key Data platform services would like to highlight

  • 1. Azure Data Factory (ADF)
  • 2. Azure Synapse Analytics
  • 3. Azure Stream Analytics
  • 4. Azure Databricks
  • 5. Azure Cognitive Services
  • 6. Azure Data Lake Storage
  • 7. Azure HDInsight
  • 8. Azure CosmosDB
  • 9. Azure SQL Database

#azure-databricks #azure #microsoft-azure-analytics #azure-data-factory #azure series

Azure Series #1: Security Layer — 2. Network — Protection

Protection:

Web Application Firewall:

Azure Web Application Firewall (WAF) provides centralized protection on the Azure Application gateway. The attackers who try to get into the web servers and tries to disrupt the services are protected via WAF. The attacks and vulnerabilities include SQL Injection, cross-site scripting, etc. The interesting part is, WAF automatically updates to include protection against any new vulnerabilities with no configuration needed at all.

Key Benefits:

  1. Protection
  2. Monitoring
  3. Customization

Key Features:

  • Vulnerabilities / Attacks: SQL-Injection protection & Cross-site protection, HTTP request smuggling, HTTP response splitting and remote file inclusion, HTTP Protocol violations, HTTP protocol anomalies, crawlers, and scanners.
  • Mis-Config: Protection against misconfiguration in web servers, incorrect size limits.
  • Filters: Geo-filter traffic, block or open certain countries/regions for your organization’s applications.
  • Rules: create WAF policies to enable WAF for your application.

Azure Firewall:

While WAF is for Application security, you need a security and protection layer that is for the Network, which is taken care of by Azure Firewall — it is a cloud-based network security service that protects your organization’s Azure Virtual Network Resources. It is fully stateful in the sense that inbound requests trace outbound responses. Across your organization’s subscription and virtual networks, you can enforce, create and log application and network connectivity policies. It uses Static IP for your virtual network sources allowing outside firewalls to identify traffic from the virtual network and is fully integrated for Azure monitor for logging and analytics.

#azure-interview #azure-security #azure series #azure #network #protection

Sean  Doyle

Sean Doyle

1624930125

Recommendation Engine and Azure ML Series - Introduction to Azure ML

This session will be an introduction to AzureML and we will introduce the concepts of Azure ML by building a model using Azure ML and deploying it in Azure Kubernetes Services. This session will focus on Azure ML - Training and Deploying a Model into Azure Kubernetes Service from scratch.

Speaker BIO- Ambarish is a Business and Technology Consultant for more than 20 Years. He is a data lover and compete regularly in data science competitions. He had won Eight Data Science Competition Awards ( 1 sponsored by NASA, Seven sponsored by Kaggle - A Google Company). He presently works with Tata Consultancy Services (TCS) as the Energy Trading and Risk Management Lead, as well as Data Analytics Practice Lead for TCS Utilities. This is a very exciting position where he has the opportunity to combine domain knowledge with data. He is very fortunate to be surrounded by very enthusiastic and energetic people. He do follow cricket, loves to read detective books, and also watch detective films.
Social Handles-
LinkedIn- https://www.linkedin.com/in/ambarish-ganguly/
Twitter- https://twitter.com/a_ganguly/

#machine-learning #azure

Eric  Bukenya

Eric Bukenya

1624713540

Learn NoSQL in Azure: Diving Deeper into Azure Cosmos DB

This article is a part of the series – Learn NoSQL in Azure where we explore Azure Cosmos DB as a part of the non-relational database system used widely for a variety of applications. Azure Cosmos DB is a part of Microsoft’s serverless databases on Azure which is highly scalable and distributed across all locations that run on Azure. It is offered as a platform as a service (PAAS) from Azure and you can develop databases that have a very high throughput and very low latency. Using Azure Cosmos DB, customers can replicate their data across multiple locations across the globe and also across multiple locations within the same region. This makes Cosmos DB a highly available database service with almost 99.999% availability for reads and writes for multi-region modes and almost 99.99% availability for single-region modes.

In this article, we will focus more on how Azure Cosmos DB works behind the scenes and how can you get started with it using the Azure Portal. We will also explore how Cosmos DB is priced and understand the pricing model in detail.

How Azure Cosmos DB works

As already mentioned, Azure Cosmos DB is a multi-modal NoSQL database service that is geographically distributed across multiple Azure locations. This helps customers to deploy the databases across multiple locations around the globe. This is beneficial as it helps to reduce the read latency when the users use the application.

As you can see in the figure above, Azure Cosmos DB is distributed across the globe. Let’s suppose you have a web application that is hosted in India. In that case, the NoSQL database in India will be considered as the master database for writes and all the other databases can be considered as a read replicas. Whenever new data is generated, it is written to the database in India first and then it is synchronized with the other databases.

Consistency Levels

While maintaining data over multiple regions, the most common challenge is the latency as when the data is made available to the other databases. For example, when data is written to the database in India, users from India will be able to see that data sooner than users from the US. This is due to the latency in synchronization between the two regions. In order to overcome this, there are a few modes that customers can choose from and define how often or how soon they want their data to be made available in the other regions. Azure Cosmos DB offers five levels of consistency which are as follows:

  • Strong
  • Bounded staleness
  • Session
  • Consistent prefix
  • Eventual

In most common NoSQL databases, there are only two levels – Strong and EventualStrong being the most consistent level while Eventual is the least. However, as we move from Strong to Eventual, consistency decreases but availability and throughput increase. This is a trade-off that customers need to decide based on the criticality of their applications. If you want to read in more detail about the consistency levels, the official guide from Microsoft is the easiest to understand. You can refer to it here.

Azure Cosmos DB Pricing Model

Now that we have some idea about working with the NoSQL database – Azure Cosmos DB on Azure, let us try to understand how the database is priced. In order to work with any cloud-based services, it is essential that you have a sound knowledge of how the services are charged, otherwise, you might end up paying something much higher than your expectations.

If you browse to the pricing page of Azure Cosmos DB, you can see that there are two modes in which the database services are billed.

  • Database Operations – Whenever you execute or run queries against your NoSQL database, there are some resources being used. Azure terms these usages in terms of Request Units or RU. The amount of RU consumed per second is aggregated and billed
  • Consumed Storage – As you start storing data in your database, it will take up some space in order to store that data. This storage is billed per the standard SSD-based storage across any Azure locations globally

Let’s learn about this in more detail.

#azure #azure cosmos db #nosql #azure #nosql in azure #azure cosmos db

Eric  Bukenya

Eric Bukenya

1624106940

Azure Series: Multi-part series on Azure Cloud and related guidelines

In this multi-part Azure Cloud series, I intend to cover the general aspects of Azure in simple terms, the business case for cloud, some deep dives where required, migration strategy, AllOps, security by design framework, reference architectures, and/or demo, and more. I am putting up a Lego bricks approach with multiple layers (in conjunction with the OSI / TCP/IP Layer) and will be adding several Reference architectures (for Web, Batch, Mobile, Data Lake, Big Data, Machine Learning, etc) after assorting and categorizing these Lego pieces. Along the way, I will also discuss the adoption of cloud for different sizes of organizations and building a cloud for scale and how best can make it built to last and at the same time extend it to handshake with other cloud providers to enable Poly Cloud / Multi-Cloud based adoption for the organization.

#azure #azure-data-lake #azure-devops #azure-interview