CODING & ANALYTICS by Gbenga Oridupa

  The Azure Storage platform is Microsoft's cloud storage solution for modern data storage. The services are: Durable and highly available: Redundancy ensures that your data is safe in the event of transient hardware failures. You can also opt to replicate data across datacenters or geographical regions for additional protection from local catastrophe or natural disaster. Data replicated in this way remains highly available in the event of an unexpected outage. Secure: All data written to an Azure storage account is encrypted by the service. Azure Storage provides you with fine-grained control over who has access to your data. Scalable: Azure Storage is designed to be massively scalable to meet the data storage and performance needs of today's applications. Managed: Azure handles hardware maintenance, updates, and critical issues for you. Accessible: Data in Azure Storage is accessible from anywhere in the world over HTTP or HTTPS. Microsoft provides client libraries for Azure ...

  What is Azure SQL? Azure SQL is a family of managed, secure, and intelligent products that use the SQL Server database engine in the Azure cloud. Azure SQL Database: Support modern cloud applications on an intelligent, managed database service, that includes serverless compute. Azure SQL Managed Instance : Modernize your existing SQL Server applications at scale with an intelligent fully managed instance as a service, with almost 100% feature parity with the SQL Server database engine. Best for most migrations to the cloud. SQL Server on Azure VMs: Lift-and-shift your SQL Server workloads with ease and maintain 100% SQL Server compatibility and operating system-level access. Azure SQL is built upon the familiar SQL Server engine, so you can migrate applications with ease and continue to use the tools, languages, and resources you're familiar with. Your skills and experience transfer to the cloud, so you can do even more with what you already have. What do you want to do? Suppor...

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 ca...

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)  tha...

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 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!.

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...

Can your IoT solution grow with you? Are you protecting data on devices and in the cloud? Azure IoT Central is built with the properties of highly secure and scalable IoT solutions. New Azure IoT Central features available: 1. 11 new industry-focused application templates to accelerate solution builders across retail, healthcare, government, and energy industries. 2. Public APIs to access Azure IoT Central features for device modelling, provisioning, lifecycle management, operations, and data querying. 3. Management for edge devices and IoT Edge module deployments. 4. Seamless device connectivity with IoT Plug and Play. 5. Application export to enable application repeatability. 6. Extensibility from no/low code actions to data export to Azure PaaS services. 7. Manageability and scale through multitenancy for both device and data sovereignty without sacrificing manageability. 8. User access cont...

The deal is expected to close later this year, at which point Looker will become part of Google Cloud , helping serve Google customers a more comprehensive analytics solution. Data remains an untapped resource for many organizations and businesses. I believe the addition of Looker to Google Cloud will provide customers with a more comprehensive analytics solution,from ingesting and integrating data to gain insights, to embedded analytics and visualizations enabling enterprises to leverage the power of analytics, machine learning and AI. Whether that’s supply chain analytics in retailing; media analytics in entertainment; or healthcare analytics at global scale. The market for business intelligence software is large. Indeed in August 2018, IDC said that "Worldwide revenue for big data and business analytics (BDA) solutions was $166 billion, up 11.7% over 2017 and would reach $260 billion ...

The data analytics race has just taken a very interesting turn with the Tableau acquisition by Salesforce. In 2003, Tableau set out to pioneer self-service analytics with an intuitive analytics platform that would empower people of any skill level to work with data. Now acquired by Salesforce. The acquisition is the largest in Salesforce history, and the company’s co-CEO, Marc Benioff, hinted at larger ambitions when he declared that Seattle would become Salesforce “HQ2” as a result of the Tableau deal. The West Coast is increasingly becoming a larger tech hub encompassing San Francisco, Silicon Valley and Seattle. This acquisition combines a CRM and an analytics platform. Tableau helps people see and understand data, and Salesforce helps people engage and understand customers. “Joining forces with Salesforce will enhance our ability to help people everywhere see and understand data,” added...

Got your data in Google Sheets? You can connect directly to your data in Tableau using it's web data connector. This feature has been around for a while now. If you are new to it, this could be a plus for you. Select the option under “More Servers…” on the Connect menu. After entering your Google Sheets credentials, you will see the list of available sheets. You can Select from the list or use the search bar to be specific, and click “connect.” What's cool about it is that If you aren’t sure if this is exactly the sheet you are looking for, you can easily open it in a web browser by selecting the “open in Google Drive”. Once the sheet has been loaded into Tableau, you can drag out the individual sheets from your Google Sheet and join or union them together. Additionally, you can also union and join Google Sheets with other data sources.Sounds cool right?. In Tableau, you can see all th...

Let us start with a quick introduction. Amazon simple storage service or Amazon S3 provides developers / IT teams / Organizations / Individuals with secure, available, price friendly object storage in the cloud. Ok. Slow down. What is object storage? It is a computer data storage architecture that manages data as objects, as opposed to other storage architectures like file systems, which manage data as a file hierarchy, and block storage, which manages data as blocks within sectors and tracks. Amazon S3 is easy to use and can be used to store and retrieve any amount of data at any time. All data transfers are over automatic encryption and SSL. Data is secure, available when needed and will scale as your needs grow. Last thing you want to worry about is losing valuable data. Amazon S3 automatically replicates your objects on multiple devices across multiple facilities. In Amazon S3, you create bucke...

In today’s post, I will like to talk about considerations for choosing to use Azure Blob Storage or Azure Data Lake Store when processing data to be loaded into a data warehouse. Here is a Data Warehouse Architecture published by Microsoft, where it suggests loading data from your source into Azure Blob Storage. Pause for a second!!! Ok, Let's continue!!! So, here are my thoughts on why you may choose one over the other based on my experience in some projects. It really "depends".In most cases you can’t go wrong either way because they are both powerful storage systems.Let's go: Firstly, Text files. ADLS is better with text files compared to ABS.When you talk about non-text data like media files,database backup files etc, are better off with ABS.There are trade-offs with both. Secondly, Geographic redundancy. ABS gives you that out of the box. For ADLS,i...

Microsoft recently introduced the use of templates in Azure Data Factory(ADF). I believe this is a very solid implementation. By having this addition, data engineers can easily get started quickly with building data factory pipelines and improve developer productivity along with reducing development time for same workflows repeatedly. Ready to explore?. Let's jump in for a cool ride: 1. Just click Create pipeline from template on the Overview page or click +-> Pipeline from template on the Author page in your data factory UX to get started. 2. Select any template from the gallery and provide the necessary inputs to use the template. You can also read detailed description about the template or visualize the end to end data factory pipeline. 3. You can also create new connections to your data store or compute while providing the template inputs. 4. Once you cli...

My major Cloud Computing players; AWS, Azure and Google. Choosing one is the most difficult task for those that wants to enter and grow in the cloud world. With the continuous growing importance of Cloud Computing services nowadays, demand is on the high side. Cloud service providers supply resources like database, applications, and storage over the internet to reduce the cost of managing and maintaining your IT systems. Services/Products offered: 1. Software as a Service (SaaS): Software as a service is a software licensing and delivery model in which software is licensed on a subscription basis and is centrally hosted. 2. Infrastructure as a Service (IaaS): Infrastructure as a service refers to online services that provide high-level APIs used to dereference various low-level details of underlying network infrastructu...

Welcome back guys. Just like i said in my previous post, getting started with Data Analysis Expressions (DAX) can be intimidating, but becoming knowledgeable will guide you through unlocking new insights into your data. I believe the following DAX functions can get you started on the right path. Let’s work through some common business scenarios. 1. FILTER: The FILTER function is used to return a subset of a table or expression, as shown below. Let’s say that you want to get a count of items sold based on specific amount range.e.g Amount between 150 and 250. We will use the COUNTROWS function (Just like the name, it counts record), which counts the number of rows in the specified table, along with the FILTER function to achieve this: Count of sales between 150 and 250 = COUNTROWS(FILTER('Sales', 'Sales'[Sales...

This is just an overview of DAX language . We will begin calculations and extensive analysis on my next post.Enjoy. What is DAX?: Data Analysis Expression(DAX) is a Functional Language currently used in Power BI Desktop ,Analysis Services Tabular and Power Pivot for Excel. It is a collection of functions that can be used to calculate and return one or more values. In DAX, functions can contain other, nested functions and value references depending on your model and key business questions to be answered. DAX includes some of the functions used in Excel formulas plus other functions designed specifically to work with relational data and perform dynamic aggregations. Why DAX?: The difficult questions raised facing a business or an organization can be simplified using DAX, to shed more light into your data. Learning how to create effective DA...

So people have been asking me to give some details on MapReduce concept. This is a very interesting topic to write about. If you have read my previous post, you would have seen my introduction to Big Data and Hadoop. Now I am going to talk about MapReduce has the heart of Hadoop. Some of you might be new to this, but do not worry, it is going to be described in a way you will quickly understand. To Java developers, it might be much easier, but if you do not have experience in java skills, you can still learn some basic java and master MapReduce. MapReduce is a programming framework that allows performance of distributed and parallel processing on large data sets in a distributed environment. I am talking massive scalability across hundreds or thousands of servers in a Hadoop cluster. Just imagine that for a second. If you see in the diagram above, we have the “Input, Map task, Reduce task ...