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Showing posts from 2020

Connector for AWS in Azure Cost Management + Billing

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In 2019, Microsoft announced the preview of the connector for Azure Cost Management + Billing, which allows customers to analyze their Azure and AWS spend from a single pane of glass in the Azure Portal.  Guess what?.... the feature is now generally available. This new connector simplifies handling different cost models and numerous billing cycles so you can visualize and always stay up-to-date with your spend across clouds.   Let's get started. Setting up the connector in a few quick steps: Setup and configure an AWS cost and usage report in the AWS portal. Create a role and policy in AWS, which provides Azure Cost Management with access as well as permissions proving organization API access and cost explorer API access. Lastly, set up the AWS connector in Azure Cost Management + Billing. You can view your AWS costs within Cost Analysis in the following scopes: AWS Linked accounts under a management Group. AWS Linked account costs. AWS Consolidated account costs. You can view you

Apache Cassandra in the Cloud : Amazon Keyspaces and Datastax Astra

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Apache Cassandra is a distributed database that delivers the high availability, performance, and linear scalability today’s most demanding applications require.  It offers operational simplicity and effortless replication across cloud service providers, data centers, and geographies, and it can handle petabytes of information  and thousands of concurrent operations per second across hybrid cloud environments. The arrival of managed cloud services to Cassandra is key to making this high-performance, highly-scaled distributed database accessible to a wider audience.  Cassandra has long been known for its performance and scale, but never for its ease of use. Given those hurdles,  But, as the popularity of AWS's DynamoDB service shows, there is strong demand for distributed databases. The fact is, managed cloud services eliminate, patches, maintenance, and upgrades.  The management API wraps an abstraction layer around the JMX (Java Management Extensions)  that Cassandra uses to provi

Schema-on-Write vs Schema-on-Read

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Since the inception of Relational Databases in the 70’s, schema on write has be the defacto procedure for storing data to be analyzed. However recently there has been a shift to use a schema on read approach, which has led to the exploding popularity of Big Data platforms and NoSQL databases. Any data management system belongs to one of two types: Schema-on-write: Probably a lot of you have already worked with relational databases and you understand that once we have configured the schemas, created the tables, we can begin to ingest the data. Remember just because the data is structured doesn’t mean it starts out that way. It is likely to be something like bulk upload data from a text or csv file whose structure we know in advance because it somehow matches the schema of the tables, and once the data is loaded into the table, we can begin to execute analytical queries on our tables. This

Facebook to buy Giphy for $400 million

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Facebook has agreed to buy Giphy , the popular platform of shareable animated images. The total deal value is around $400 million. A source close to the situation says that the two companies first began talking prior to the pandemic, although that was more about a partnership than an acquisition. New York-based  Giphy is expected to retain its own branding, with its primary integration to come via Facebook's Instagram platform. Facebook is currently facing enormous blowback over its previous acquisitions, which means that this deal, however small by comparison, is likely to face a lot of antitrust scrutiny by regulators. The tech giant is currently under investigation by federal and state lawmakers for antitrust. Giphy is a massive video library, with hundreds of millions of daily users that share billions of GIFs. If you still have questions or just want to chat about Tech stuff, contact me and i will be glad to help!.

Bringing Kubernetes to Windows Server apps(Google Cloud Platform)

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Firstly, before we begin. For those that don't already know. What is Google Kubernetes Engine ?: GKE is an enterprise-grade platform for containerized applications, including stateful and stateless, AI and ML, Linux and Windows, complex and simple web apps, API, and backend services. Leverage industry-first features like four-way auto-scaling and no-stress management. Optimize GPU and TPU provisioning, use integrated developer tools, and get multi-cluster support from SREs. Now that we know GKE, the purpose of this post is about running Windows Server apps as containers on Kubernetes, where you get many of the benefits that Linux applications have enjoyed for years. Running your Windows Server containers on GKE can also save you on licensing costs, as you can pack many Windows Server containers on each Windows node. In the beta release of Windows Server container support in GKE (version 1.16