One of the most useful features of Terraform is its ability to import existing resources into your infrastructure as code. This allows you to take control of resources that may have been created manually or by another team and bring them under the management of your infrastructure as code. However, the process of importing resources can be time-consuming and error-prone, particularly if you are dealing with many resources or complex configurations. This is where Terraformer comes in.

What is Terraformer?

Terraformer is an open-source tool written in Go Language, originally developed by Waze SRE that automates the process of importing existing resources into Terraform. It allows you to generate Terraform code from existing resources, making it easy to manage them using Terraform. Terraformer supports a wide range of cloud providers, including AWS, GCP, Azure, Kubernetes, and more. Terraformer currently supports sixteen clouds and more than fifteen providers like Datadog, Kubernetes, PagerDuty, GitHub, and more.

How does Terraformer stand apart from its competitors?

1. Terraformer differs from other competitors in a few keyways.
2. Terraformer is a command-line tool, while some of its competitors are web-based tools. This makes Terraformer more portable and easier to use in a CI/CD pipeline.
3. Terraformer eliminates the manual intervention needed in other IaC tools by automatically generating configurations after importing the infrastructure.
4. Terraformer supports a wider range of infrastructure sources than some of its competitors. Terraformer currently supports AWS, Azure, GCP, and Kubernetes, while some of its competitors only support a subset of these providers.
5. Finally, Terraformer is easier to use as it has a simpler user interface and provides more helpful documentation.

How to use Terraformer?

Using Terraformer is straightforward. First, you need to install it on your local machine. You can do this using the command-line interface (CLI) or a Docker container. Once installed, you can use Terraformer to generate Terraform code for existing resources.

To install Terraformer on a Linux machine, run the below commands:

export PROVIDER={aws}

curl -LO https://github.com/GoogleCloudPlatform/terraformer/releases/download/$(curl -s https://api.github.com/repos/GoogleCloudPlatform/terraformer/releases/latest | grep tag_name | cut -d ‘”‘ -f 4)/terraformer-${PROVIDER}-linux-amd64

By running the below command, you can check the Terraformer version.

terraformer -v

To import resources with an AWS provider, first authenticate to the AWS account in which the resource is located. To configure Terraformer with your AWS credentials, you would use the following command

terraformer configure –provider aws –credentials ~/.aws/credentials

AWS configuration, including environmental variables, a shared credentials file (~/.aws/credentials), and a shared configuration file (~/.aws/config) will be loaded by the tool by default. To use a specific profile, you can use the following command:

After authenticating to the AWS account, run terraform init against a provider.tf file to install the plugins required for your platform.

terraform {

  required_providers {

    aws = {

      source  = “hashicorp/aws”

      version = “~> 4.0”

    }

  }

}

provider “aws” {

  region = “us-east-1”

}

To import all AWS Elasticache and RDS into Terraform, use the below command:

terraformer import aws –-path-pattern=”{output}/” –compact=true –regions=eu-central-1 –resources=elasticache,rds

The above command tells Terraformer to import all the Elastic cache and RDS in the region eu-central-1 and generate Terraform code, and the –compact=true flag tells Terraformer to write all the configurations in a single file.

Terraformer also supports importing multiple resources at once, and you can use filters to import only specific resources that meet certain criteria.

For example, you can import all AWS EC2 instances that have a certain tag by using the following command:

terraformer import aws –path-pattern=”./ec2/” –compact=true –regions=eu-central-1 –resources=ec2_instance –filter=”Name=tags.NodeRole;Value-node”

The above command tells Terraformer to create Terraform code in the directory./ec2/ and import all EC2 instances with the tag Noderole=node.

By default, Terraformer separates each resource into a file that is placed in a specified service directory. Each provider may have a different default path for resource files, which is {output}/{provider}/{service}/{resource}.tf.

We can manage the resources with Terraform using the plan, apply, and remove actions now that the configuration files have been created with Terraformer.

Also Read this informative blog on How to Secure an AWS Environment with Multiple Accounts.

Benefits of using Terraformer

Achieve Infrastructure as Code: Terraform promotes the principle of infrastructure as code, where infrastructure resources are defined in a declarative language. The tool allows users to import existing resources into Terraform, making it easier to maintain a consistent and reproducible infrastructure state.

Version and manage resources: By importing resources into Terraform, users can take advantage of Terraform’s versioning and management capabilities. This includes tracking changes, applying modifications, and collaborating on infrastructure changes through version control systems.

Apply infrastructure changes: With imported resources, users can modify and apply changes to their infrastructure using Terraform. This provides a standardised and automated way to manage the lifecycle of resources, ensuring consistency and reducing the risk of manual errors.

Leverage the Terraform ecosystem: Importing resources into Terraform allows users to leverage the extensive ecosystem of Terraform providers, modules, and other tooling. This enables the use of various integrations and extensions to enhance infrastructure management and provisioning.

Conclusion

Terraformer is a valuable tool for organizations that are looking to improve the speed, efficiency, and reliability of their infrastructure deployments. By automating the process of converting existing infrastructure to Terraform configuration files, providing a consistent and repeatable way to provision infrastructure resources, and enabling organizations to track changes to infrastructure resources and to roll back changes, if necessary, Terraformer can help organizations to save time and reduce the risk of errors and disruptions.

The post Terraformer: A Powerful Tool for Importing Infrastructure into Terraform appeared first on Indium.

The blog will go through the following topics:

• What is Big Query?
• Best features of Big Query?
• Why BQML? Areas of BQML?
• Regression model to show efficiency of BQML

Let’s dive into the article.,

What is Big Query?

With built-in technologies like machine learning, business intelligence and geospatial analysis, Big Query is a managed service data management warehouse that can enable you to manage and analyse your data. With no need for infrastructure administration, Big Query’s serverless architecture enables you to leverage SQL queries to tackle the most critical issues facing your company. You may query data in terabytes in a matter of seconds and petabytes of data in a matter of minutes thanks to Big Query’s robust, distributed analytical engine.

Best features of Big Query?

Built-in ML Integration (BQ ML), Multi cloud Functionality (BQ Omni), Geospatial Analysis (BQ GIS), Foundation for BI (BQ BI Engine), Free Access (BQ Sandbox), Automated Data Transfer (BQ Data Transfer Service). These are the amazing features of BQ, in this blog we will discuss the most amazing feature of Big Query which is Big Query ML.

*An amazing feature of Big Query is Big Query ML,

Big Query ML allows you to use standard SQL queries to develop and run machine learning models in Big Query. Machine learning on huge datasets requires extensive programming and ML framework skills. These criteria restrict solution development within each organization to a small group of people, and they exclude data analysts who understand the data but lack machine learning and programming skills. This is where Big Query ML comes in handy; it allows data analysts to employ machine learning using their existing SQL tools and skills. Big Query ML allows analysts to create and evaluate machine learning models in Big Query with large volumes of data.

Why BQML?

The major advantages I’ve identified using BQML

• There is no need to read your data from local memory because, like any other ML language, BQML can subsample your dataset, but BQML can also train your model directly in your database.
• Working in SQL can help you collaborate more easily if you’re working in a team and the majority of your teammates don’t know Python, R, or your favourite modelling language. 
• Because your model will be in the same location as your data, you can serve it immediately after it has been trained and make predictions directly from it.

Areas we can use BQML

• Retail Industry (Demand forecasting, Customer segmentation, Propensity to purchase or propensity to click on item, Product recommendations by emails and ads).
• Logistics Industry (Time estimation of package delivery, Predictive maintenance).
• Finance Industry (Product recommendations by emails and ads, Product recommendations by emails and ads, Product recommendations by emails and ads, Product recommendations by emails and ads).
• Gaming Industry (Content recommendation, Predicting churn customers).

 Another blog worth reading: Databricks Overview, Why Databricks, and More

Regression model to show efficiency of BQML

• For this we will build a linear regression model to predict the house prices in the USA, as it is best fit to predict the value of one variable using another. Also, for understanding about model working in the article I am using example of regression model as it is simpler to communicate how the model itself works and interpret results.
• With the USA housing dataset, we will see how efficient and easy Big Query ML feature is to build machine learning linear regression model with SQL code.

Step 1: Creating the Model

CREATE OR REPLACE MODEL

`testproject-351804.regression.house_prices2` OPTIONS(model_type = ‘linear_reg’, input_label_cols = [‘price’],l2_reg = 1, early_stop = false, max_iterations = 12, optimize_strategy = ‘batch_gradient_descent’) ASSELECT avg_house_age, avg_rooms, avg_bedrooms, avg_income, population, price/100000 AS priceFROM `regression.usa_housing_train`

SELECT avg_house_age, avg_rooms, avg_bedrooms, avg_income, population, price/100000 AS price FROM  `regression.usa_housing_train

Model creation

• The above code will create and train the model.
• With the simple CREATE MODEL function we can create the ML model, you need to specify the OPTIONS, we need basically only model_type and input_label_cols(predicting variable) to create the model but why I used other OPTIONS, you will see in evaluation section.

Step 2: Evaluating the Model

SELECT * FROM ML.EVALUATE(MODEL `regression.house_prices2`,TABLE ` testproject- 351804._8b41b9f5a2e85d72c62e834e3e9dd60a58ba542d.anoncb5de70d_1e3d_4213_8c5d_bb10d6b9385b_imported_data_split_eval_data`)

Model Evaluation

• We have to see how well our model is performing by using ML.EVALUATE funtion, So now we will see why I used other OPTIONS in creating the model,
• First I created a model in BigQuery ML, with model options model_type= ‘linear_reg’ and input_label_cols = ‘price’ but while evaluating the model “r square” is only 0.3 which I felt less accurate and I came to know that model is overfitt by seeing huge difference between the training loss and evaluation loss.
• So, as a solution I added options in creating model, used L2 regularization to overcome overfitt and generalize the model to adapt the data points and changed values for three times to made it generalize and after the r square is 0.92 with above 90% accuracy.

*We need to look upon R-Squared, which is Coefficient of determination. Higher is better.

Step 3: Predicting the Model

The model’s prediction process is as simple as calling ML.PREDICT

SELECT * FROM ML.PREDICT (Model `regression.house_prices2`,TABLE `regression.usa_housing_predict`)

Model Prediction

See, how efficient is Big Query ML feature of Big Query, it predicted the house prices basing upon the trained data of avg_house_age, avg_rooms, avg_bedrooms, avg_income, avg_population.

Summary

Now you know how to create linear regression models in BigQuery ML. We have discussed how to build a model, assess it, apply it to make predictions, and analyse model coefficients.

In next coming blogs you will see other unique features of Big Query like Geospatial Analytics and Array/Structs.

Happy Reading

Hope you find this useful.

The post Overview of Big Query’s Unique feature, BQML with a regression model example appeared first on Indium.

In a shift-left approach, DevSecOps is becoming popular, where security is introduced earlier in the application development life cycle. This facilitates a collaborative approach by integrating security with development with deployment and making security a shared responsibility. Security becomes the responsibility of all those who are part of the SDLC and the DevOps services: continuous integration and continuous delivery (CI/CD) workflow.

Read this amazing blog on: Shifting From DevOps to DevSecOps

Security with Speed and Quality

As the time to market shrinks, the need to deliver products quickly and with quality takes priority. By integrating security during the application development lifecycle using a DevSecOps approach, developers can deliver secure applications. DevSecOps encompasses the entire development life cycle from planning to designing, coding, building, testing, and release. Usually, security is added at the end, but fixing security issues post-production can be costly and time-consuming, not to mention delaying the release. DevSecOps prevents this by allowing testing, triaging, and risk mitigation to be incorporated into the CI/CD workflow. This way, security issues can be fixed in real-time in the code instead of being added at the end.

DevSecOps with Google Cloud Platform

Google Cloud’s built-in services enable the development of a secure CI/CD pipeline. Initially, the developers commit the changes to the code to a source code repository, which triggers the delivery pipeline automatically. It also builds and deploys the code changes into various environments, from non-prod environments to production.

The security aspect is also incorporated into the pipeline right at the beginning with open-source libraries and container images when building the source code. By integrating security safeguards within the CI/CD pipeline, the software being built and deployed can be free from vulnerabilities. This also helps determine the type of code/container image that should be permitted to be deployed on the target runtime environment.

Also read: 5 Best Practices While Building a Multi-Tenant SaaS Application using AWS Serverless/AWS EKS

The Google Cloud built-in services that enable the building of a secure pipeline include:

Cloud Build – A serverless CI/CD platform, it facilitates automating building, testing, and deploying tasks.

Artifact Registry – A secure service that allows the storing and managing of your artifacts.

Cloud Deploy – A fully managed Continuous Delivery service for GKE and Anthos.

Binary Authorization – Providing deployment time security controls for GKE and Cloud Run deployments.

GKE – A fully managed Kubernetes platform.

Google Pub/Sub – A serverless messaging platform.

Cloud Functions – A serverless platform for running the code.

The CI/CD pipeline can be set up without enforcing the security policy. But to integrate security with the design and development, the process involves:

• Allowing vulnerability scans to be performed on Artifact Registry and using the Binary Authorization service for creating a security policy.
• Deploying a specific image to the GKE cluster by the developer by checking in the code to a GitHub repo.
• Configuring a Cloud Build trigger to detect the checking in of any new code to the GitHub repo and begin the ‘build’ process.
• The failing of the build process and the triggering of an error message notifying the presence of vulnerabilities in the image.
• When a Binary Authorization policy is violated, an email is sent to a pre-configured email id about the deployment failure.

Cloud Build and Deply Capabilities of Google Cloud

GCP’s Cloud Build enables importing source code from different repositories and cloud storage spaces, executing a build based on specifications, and producing artifacts such as Java archives or Docker containers.

Cloud Build also protects the software supply chain as it complies with the supply chain Levels for Software Artifacts (SLSA) level 3.

Cloud Build features enable securing the builds using features such as:

Automated Builds: In an automated or scripted build, all steps are defined using build script or configuration, including how to retrieve and build the code. The command to run the build is the only manual command used. A build config file is used to provide the Cloud Build steps. Automation ensures consistency of build steps and improves security.

Build Provenance: The provenance metadata is a source of verifiable data about a build and provides details such as:

• Digests of the built images
• Input source locations
• Build toolchain
• Build duration

This helps ensure that the built artifact is from a trusted source location and build system. Build provenance can be generated in Cloud Build for container images with SLSA level 2 assurance.

Ephemeral Build Environment: Ephemeral environments or temporary environments enable a single build invocation, after which the build environment is deleted and leaves behind no residual files or environment settings. This prevents the risk of attackers injecting malicious files and content, reduces maintenance overhead, and decreases inconsistencies in the build environment.

Deployment Policies: By integrating Cloud Build with Binary Authorization, build attestations and block deployments of images not generated by Cloud Build can be verified. This reduced the risk of unauthorized software being deployed.

Customer-Managed Encryption Keys: Compliant customer-managed encryption keys (CMEK) is a default feature in Cloud Build that eliminates the need for users to configure anything specifically. The key is generated uniquely for each build by the encryption of build-time persistent disk (PD) with a temporary key generated every time. This key is destroyed and removed from memory after the completion of the build and the data protected by such a key is inaccessible forever.

Google Cloud Deploy: Google Cloud Deploy is a managed infrastructure deployment service for automating the creation and management of Google Cloud resources. It automates the delivery of applications to a series of target environments in a pre-defined sequence. It ensures GKE and Anthos Continuous Delivery, and once the build is ready, a Cloud Deploy pipeline is created. This will deploy the container image to the three GKE environments of testing, staging, and production. It requires an approval process to be implemented, ensuring security.

Indium–for DevSecOps with GCP

Indium Software is a leading software solution provider offering a comprehensive set of DevOps services to increase the high-quality throughput of new capabilities. The solutions offered include:

CI/CD Services: Create code pipelines free of blocks and with a smooth value stream flowing from development to integration, testing, security, and deployment

Deployment Automation: Automate deployment and free up resources to perform value-added tasks

Containerization: Packaged executables that allow build anywhere-deploy anywhere approach

Assessment & Planning: Establish traceable metrics to assess performance and achieve the desired state

Security Integration: Ensure end-to-end security integration with ‘Security as Code’ using DevSecOps

The post <strong>Implementing DevSecOps with GCP’s Built-in Tools and Solutions</strong> appeared first on Indium.

From religious apps, internal employee communications apps, fitness apps, e-commerce apps, small business apps, and more, there is always an app for anything you can think of. You can create an app as an extension for your business or use it to establish a new business.

So, whether you want to create the next Uber or build an app for your business, this is a comprehensive guide to creating a powerful mobile app. If you have experienced a failed app development attempt before, non-technical user, or are a first-time builder, we will guide you through the app-building process with Mendix in a straightforward manner.

First, let us gain a deeper understanding of the Mendix platform and then dive into building powerful mobile apps on the cloud

What is Mendix Mobile?

Mendix is a low-code platform that allows organizations to develop mobile apps and deploy them elsewhere. Also, it enables organizations to leverage modern mobile technologies, offer rich native user experiences, and easily use native device features.

Mendix platform backs up the entire application development lifecycle from ideation, development, and deployment to monitoring. Mendix assists developers in building rich mobile apps from a single model, with a single platform, and with no code. In that case, enterprises can build mobile apps without the extended technical know-how.

Mendix apps are packaged and published as mobile apps through PhoneGap. In this framework, you can generate a mobile app for multiple platforms like iPhone Operating System (iOS) and Android, install it on tablets and phones, and publish it in app stores. PhoneGap permits you to create mobile apps based on JavaScript, Cascading Style Sheets (CSS), and Hypertext Markup Language (HTML) and then deploy them to various mobile devices without compromising the native app features. Also, PhoneGap allows you to easily download apps and build mobile apps in the cloud without necessarily locally installing development tools.

5 Tips to Building Mobile Apps on The Cloud with Mendix

Here are some practical steps you can use when developing mobile apps with Mendix:

Model

Building a mobile app is close to developing a standard Mendix web app. Nevertheless, there are several aspects you must consider.

Mendix incorporates a responsive front end. This implies the elements and layout will automatically conform to the screen size. In this case, the pages you model could be displayed on a mobile device. Mobile device use differs from the desktop as it has a small screen, and users use screen gestures, not the mouse. Mobile users expect discrete navigation and distinct type of connection, among others.

To deliver the best user experience, employ the tablet or mobile profile enabled in the project navigation. This navigation is utilized by model apps which allows you to develop a channel for tablets, phones, and desktops from a single model.

When building a model for mobile apps, you may consider the following six takeaways:

• Use new buttons and open or closed pages instead of microflow actions for better performance.
• Minimize the number of fields on one page, so the user does not need to scroll.
• For phones, use template grids or list views as they are touch-friendly, not data grids.
• Promote optimal performance and user-friendliness by keeping the pages simple.
• Maintain a good project overview using different microflows and mobile page modules.
• Regulate employment of transparent CSS features as they may negatively affect performance.

“Our low-code developers have great expertise working with the most popular low-code platforms. Interested in application development using low-code/no-code? Contact our expert today!

Preview and Test the Mobile App

You need to view and test the mobile application through the app-building process. There are various approaches to testing and viewing your apps, such as deploying a hybrid app on the phone, simulating a device in Google Chrome, viewing through the Mendix Developer App, or using a tablet or phone app.

You can ideally test an offline-capable mobile app in the browser. This is because the Modeler scrutinizes invalid constructions. Use the phone or tablet profile homepage to test your offline app via the browser effortlessly. But remember, you will still test the app on real devices.

Generate a Mobile App

As stated earlier, Mendix uses PhoneGap to build a mobile app. Through the developer Portal, you can use your project’s publish tab. You just need to click on publish for Mobile App Stores and be directed to your hybrid app settings. You can input icons, splash screens, descriptions, app identifiers, and names.

The Publish for Mobile App Stores button directs you to the page where you will generate the hybrid app. You can specify the environment that your app can connect to before bringing about the hybrid app. Then, you can download the app and manually upload it later or send it to PhoneGap Build directly.

Build the Mobile App

Automatic app building starts immediately after you upload or send the app to PhoneGap. This works out of the box for Android. After the build, one can download the Android Application Kit (APK file) through a QR code or download link. Keep in mind this is exclusively for development and testing purposes. For production builds or iOS builds, a developer certificate is a must-have.

Produce the App

Once your certificates are in place, you can publish your built mobile app in an app store such as Apple App Store or Google Play Store. Posting an Android app in Google Play Store only requires you to access PhoneGap Build, download the signed APK and then upload it here

You require an Application Loader accustomed to Xcode to publish in Apple App Store, which you can download from the App Store. You can augment the PhoneGap Build IPA file and send it to Apple iTunes Connect once you start the Application Loader.

Conclusion

Whether you have created several apps or it is your first app, building mobile apps is not always a walk in the park. Take advantage of the Mendix technology today and create an app with low to no coding skills. The platform will walk you through the app development process to publishing it on Google Play and Apple Stores.

The post Mendix: A Guide to Building Powerful Mobile Apps on The Cloud appeared first on Indium.

We will learn about the relevance of cloud computing solutions and the trends for the year 2022 in this post.

You might be interested in: Best Practices for Cloud Operations Management

THE WAY FORWARD

Getting the most out of your cloud services necessitates a commitment to change and agility. These many tendencies are endemic to the cloud, and they will continue to evolve at a faster rate as cloud usage grows and the cloud is calibrated to give sharper insights. By harnessing the skills and knowledge of the industry, tracking and analysing these patterns will help your company open doors. As the world embraces cloud services, these gateways will become increasingly important for long-term growth in 2021 and beyond.

Organizations are realigning their digital strategy as the epidemic reshuffles the world and enterprises. Companies who have previously been resistive to new technology have begun to accelerate their adoption of cloud services. Organizations will invest in cloud services in 2022 in order to boost worker productivity, facilitate innovation, and become future-ready.

The post The Future Of Cloud Computing : Things To Look Out For appeared first on Indium.

This is enabled by data virtualization solutions, where a virtual view of unified data from various sources is made accessible to various front-end solutions such as applications, portals, and dashboards. Data from multiple sources is abstracted, transformed, federated, without where the data is stored. This entire data engineering process involves a safe and secure implementation.

Since the organizational data from siloed systems is integrated in a logical data layer, data virtualization does not need the data to be replicated in ETL solutions. On the contrary, the data remains in the source systems while only an integrated view of all the relevant data for a specific query is pulled up from underlying source systems and presented. It is delivered to the end user in real-time, compliant with centralized security and governance.

Uses of Data Virtualization

The holistic view of enterprise wide data in real-time enables businesses across industries to simplify processes and execute them efficiently. Some of the uses of data virtualization include:

• ● A comparative analysis of business performance over the years.
• ● Regulatory compliance with traceability of historical data.
• ● Ability to search for and discover interconnected data.
• ● Replace legacy systems and app modernization.
• ● Migration to the cloud from on-premises applications.
• ● Delivering data as a service and monetizing it.

As a result of these, businesses can experience an increase in productivity, lowering of the need for development resources, quicker data access when compared to ETL processes are some of the benefits of data virtualization.

Denodo Data Virtualization for a Holistic View

Data virtualization from Denodo, which can be deployed through marketplaces such as Microsoft Azure, Amazon Web Services (AWS), Google Cloud Platform (GCP), and Docker, helps businesses achieve the goal of data integration and presentation of a unified view.

Connect to Any Source: A company stores its data in multiple sources such as databases, data warehouses, big data repositories, Excel files, and cloud applications. Denodo Data Virtualization enables pulling data from all these sources to provide a unified view.

Combine All Types of Data: Today, businesses have a mix of structured, semi-structured, and unstructured data. Denodo enables combining numerical, text, streaming, social, and flat files for deeper insights.

Personalize Data Consumption: The data can be consumed in mode best suited to the user’s needs as reports or dashboards, portals, Web, or mobile applications.

Data virtualization creates a centralized secure layer for cataloging, searching, discovering, and governing the integrated data and its relationships. This data is presented in a virtual or logical layer without the need to replicate the physical repository.

Why Denodo

Denodo’s 20 years of innovation is reflected in the fact that its enterprise-grade data virtualization with intuitive interface is used by more than 700 customers, including several large Fortune 1000 companies, in 35 industries. From supplier management to data-as-a-service, regulatory compliance, systems modernization and so on, complex business operations are simplified by this solution.

In a study commissioned by Denodo and conducted by Forrester Consulting, it was found that leveraging the Denodo data virtualization capabilities helped its customers achieve 408% return on investment and $6.8 million economic benefits in three years.

Some of the reasons Denodo is considered a leader in data virtualization include:

• ● Agility
• ● high performance data integration and abstraction across a widely hybridized environment with multiple sources on prem and cloud
• ● Cost-efficient real-time data services
• Some of the other benefits identified by the study include:
• ● 83% faster time-to-revenue
• ● 67% lower effort needed in data preparation
• ● 65% reduction in delivery times compared to ETL
• ● 95% retention rate of Denodo Platform

Denodo Data Virtualization Platform Advantages

Some of the key capabilities available on Denodo Data Virtualization platform include the availability of a logical data fabric with semantic search enabled by an active data catalog. Its enterprise wide data governance ensures data integrity and security. AI-powered smart query acceleration ensures greater accuracy and speed.

It also comes with automated cloud infrastructure management for hybrid and multi-cloud environments. It empowers business users with self-service through embedded data preparation capabilities.

Some of its unique features include:

• ● A redesigned web based user interface that provides exceptional user experience designed to business and IT users
• ● A Dynamic Query Optimizer that facilitates faster access to data using a strategy of intelligent and optimal query execution strategy
• ● Smart Query Acceleration combined with Summaries to handle complex analytical scenarios
• ● In-Memory Parallel Processing that provides unparalleled speed for faster access to data
• ● An automated lifecycle management suite for improved and efficient data management for the cloud and with PaaS for hybrid environments as well
• ● A Dynamic Data Catalog that provides seamless access to data, improves collaboration and provides automatic recommendations using ML
• ● Ensures interoperability with current cloud systems by supporting cloud standards such as OAuth 2.0, SAML, OpenAPI, GraphQL, and OData 4

Indium–A Denodo Partner

Indium Software, a partner of Denodo, brings with it deep expertise in the data virtualization platform. It designs outcome-based strategies to ensure the effective implementation of the Denodo platform by providing comprehensive services for a wide range of use cases.

The post Accelerate Your Data Virtualization Efforts on the Cloud (AWS, Azure and GCP) with Denodo appeared first on Indium.

Communication between multiple APIs is enabled by using an API integration management platform. This is required to establish a connection between two software systems for exchanging data with each other through their APIs. API integration process for modern cloud-based applications and third-party tools helps to improve employees’ productivity, facilitating:

• Automation of processes
• Making data sharing secure
• Developing enterprise software ecosystems

Two of the prime examples of API integration can be seen in payment gateways and e-commerce. When paying online for the goods purchased, the actual transaction is not visible but happens in the background, where the app verifies the payment details through a secured API connection.

In an e-commerce marketplace, the website stores information about customers, suppliers, and the goods available in different software solutions. When an order is placed, it uses API integrations to bring together all this multitude of data from different sources for seamless access and order fulfillment.

Benefits of API Integrations

In the modern enterprise where interconnectedness is very important, API integration help to prevent the formation of data silos by connecting the modern cloud-based apps.

In every department, there may be multiple APIs to perform different tasks. The API Integration Platform enables the pulling together of the best applications for every individual task and seamlessly connects them into a highly effective stack with a sleek interface.

The API integration platform also facilitates the building of a new API from an existing one. This is an improvement over the traditional practice when an API had to be built from scratch or a third-party API had to be used. But now, with the API integration platform, new APIs can be built with just a click of the button, saving you cost and time and protecting existing investments in technology.

In businesses with legacy systems, historical data may be relegated to a system that is forgotten and therefore not accessible when needed or integrate with the new systems. The API integration platform can help extract data from these old databases and servers. Even old, efficient business logic and workflows can be reused, simplifying the digital transformation process.

New apps can be created successfully using API integration platforms by even very small teams, connecting different technologies or exposing already existing integrations. The entire application development process can be automated, accelerating the development and maintenance process and optimizing resources for innovation and improvements. Their time can be better utilized on projects of strategic importance such as increasing the ROI of the existing apps. This also helps to improve the productivity of the teams.

Most importantly, the API integration platforms enable businesses to be future-ready, keeping pace with technological advancements while protecting the existing investments.

Top 5 API Integration Tools

• 1. Postman: One of the most popular API platforms, it has more than 17 million users from across 500,000 organizations globally. It is a flexible tool that facilitates collaboration across the API integration lifecycle for faster designing, developing, documenting and testing of APIs. It also makes it easy to test the different endpoints of the web services. Desktop app for MacOSX and Windows and mobile apps for both iOS and Android are available.

• 2. Apigee Edge: This is an API management platform from Google allowing the building, publishing, and monitoring of APIs. It ensures the safety of the applications with real-time security threat detection capabilities. Personalized developer portals can be created with self-service access to the developers. Automated monitoring of all the APIs provides business insights while increasing the resiliency of the application.

• 3. Improvado: Improvado is an API integration tool designed by marketers for the marketers, providing access to all real-time campaign data through a single dashboard. This is supplemented by automated report generation and customized dashboards.

• 4. IBM API Connect: An integration platform for DevOps, all the APIs and web services can be accessed using a single connector. Custom connectors using open standards such as RESTful APIs and UDDI can also be built. Data from various sources can be integrated into a central location or database.

• 5. AWS CloudTrail: Logging, tracking, and retaining API calls related events by IT is made possible by this AWS service. The real-time logs of all AWS resources provided include information on changes to configuration and the activities performed by the user on those resources or an application created with those services. CloudTrail also enables the monitoring of cloud resource compliance.

Indium to Enable API Integration for Cloud Apps

Indium Software, a cutting-edge cloud-based solution provider, helps with end-to-end app modernization solutions including the development of APIs and enabling API integration. We help businesses with asset modernization, future-proofing their legacy applications to keep pace with the changing market needs.

Indium leverages API integration platforms to make your existing technologies, agile, and future-ready, thereby protecting your investments while ensuring competitive advantage. Our team of experts can help evaluate your existing APIs, business needs, and design flexible application modernization solutions with security and functionality.

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