The core technology stack of the Google Cloud ecosystem consists of numerous tools. These tools span many categories, including containers, data analytics, storage, multicloud, computing, serverless computing, and databases.

Since many of these technologies form a cloud workflow, you need to know these tools inside out.

Here, we'll take a look at a hand-picked list of Google Cloud tools essential for running cloud-based activities successfully. They're tools you should use as a Google Cloud expert.

1. BigQuery

BigQuery is a managed data warehouse. It helps you with ingesting, storing, analyzing, and visualizing data effortlessly.

You can upload data to the warehouse in batches. You can also feed data from several sources like Google Assistant, smart gadgets, automated machines, etc.

It has built-in features like geospatial analysis, machine learning (ML), and business intelligence (BI) that together deliver actionable insights. You can use ANSI-compliant standard dialects of SQL to perform database queries.

You can store and analyze data on stored on BigQuery. Alternatively, you can use the tool for analyzing data that is stored somewhere else.

You can interact with the tool using the Google Cloud Console user interface, command-line, or API client libraries. You can master BigQuery by enrolling in Google Cloud learning programs like the Google Cloud Skills Boost.

2. Filestore

Filestore is a managed cloud storage service of Google Cloud. It facilitates low-latency file operations for applications that access data through virtual machines, Google Kubernetes Engine, or Compute Engine.

It's a cloud storage technology that can support up to 920K Input/output operations per second (IOPS). Projects such as data analytics, genomics data processing, media rendering, etc., are latency-sensitive workloads.

Therefore, you need high-speed data processing storage like Filestore. It can store up to 100TB of data with a transfer rate of 25 GB/s.

3. Persistent Disk

Persistent Disks are reliable storage options for virtual machines because they offer fast data access and automatic encryption. These are block storages consisting of HDDs and SSDs.

Persistent Disk offers flexible operation models, like real-time upscaling of the disk size without restarting the virtual machine and switching to SSD from HDD when your app requires higher IOPS.

You can attach Persistent Disk to instances that you run on Compute Engine or Google Kubernetes Engine. You can effortlessly detach the disk to keep your data when you end any instances on your virtual machines.

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Multiple virtual machines can concurrently access data from one Persistent Disk without facing latency.

4. Anthos

It’s an advanced application management platform for multicloud and hybrid development and operations. You can manage Google Kubernetes Engine clusters, workloads on virtual machines, and cloud operations on AWS through Anthos.

You don’t need to set up a hypervisor or virtual machine monitor VMM software to run Anthos on your servers, and virtual compute infrastructure. Anthos is the perfect tool to create, enforce, and automate security policies throughout all virtual machines.

For example, the Anthos Config Management always keeps the Kubernetes clusters updated with security and compliance policies.

5. Google Kubernetes Engine (GKE)

Google Kubernetes Engine (GKE) is a tool that manages the deployment and operation of containerized applications. It’s an open-source program developed on Google Cloud.

It facilitates faster and secure software development and deployment anywhere. You can use GKE for container management automation and assign human resources to tasks that matter most.

It has in-built command libraries for software deployment, updating the apps, scaling up or down as per user activities, and monitoring app performance.

6. Compute Engine

Compute Engines let you run virtual machines on Google Cloud. It facilitates the live migration of data and apps between hosts without any need for virtual machine reboots.

Therefore, critical cloud-based software keeps running even when your backend team is updating or debugging programming codes.

Google Cloud classifies Compute Engines depending on CPU cores, memory, and performance. There are up to nine variants of Compute Engines, and they bear codes like T2D, M2, N2, C2, A2, etc.

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T2Ds are ideal for web servers, large-scale java apps, media transcoding, etc. On the other hand, A2s are the highest performing Compute Engines with A100 GPU for machine learning and artificial intelligence workloads.

7. Cloud Run

It’s a serverless compute platform that enables quick development and deployment of apps on the cloud. You don’t need to worry about the infrastructure and system compatibility of your binaries or programming language.

You can write codes in any language of your choosing like Python, Java, Go, Ruby, and many more. Cloud Run makes your apps truly portable as this platform has been built on the Knative open standards.

You can effortlessly move your apps to any Kubernetes clusters, Google Cloud Platform, or any other third-party cloud solution.

8. App Engine

If you want to develop host web applications on a larger scale, App Engine is the ideal Google Cloud tool for you. Simply write a program on any supported language like PHP, Java, Go, Python, etc., and hit the gcloud app deploy.

App Engine will automatically upload and run your code on Google Cloud. It automatically scales up and down depending on app usage requests.

Therefore, app owners can save a lot through automatic scaling and not sticking to unutilized app hosting infrastructure. It also offers free SSL certificates for data transfer security for your apps, either for mobile or web.

9. Firestore

It’s essentially a NoSQL document database service on Google Cloud Platform. You can efficiently store, sync, and query data on apps for devices like IoT appliances, IoT wearables, smartphone apps, and web apps.

It also secures your database while automatically replicating the database for multi-regional apps. Your apps will reflect live changes if you modify the backend code, as Firestore operates in near real-time.

Thus, you can implement collaborative work and cross-device functionalities in your apps.

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Firestore also automatically scales up/down the demand of app data access. So, if an app is on Firestore, it won't face bizarre moments like app crashes due to billions of access requests.

10. Cloud Spanner

Cloud Spanner is a managed relational database from Google Cloud. It’s the ideal database for mission-critical apps that rely on real-time data retrieval with no latency.

Therefore, you’ll see the use of Spanner in apps that facilitate real-time online transactions and live decision-making workloads.

It offers a hybrid experience of the best attributes from relational databases and scaling from NoSQL databases. You can interact with Spanner through Google Cloud Console or the gcloud command-line interface.

11. Bigtable

Bigtable is a GCP-managed NoSQL database for large analytical work. It’s a thinly populated table that can accommodate thousands of columns and billions of rows.

You need Bigtable from Google Cloud when you’re working with big data analysis, like terabytes or petabytes of data. It facilitates fast access to large amounts of data through high read/write rates at low latency.

You can create a Bigtable instance by using the command-line interface, cloud console, or API. The Google Search engine and Google Maps uses Bigtable to deliver search results to billions of users in a flash.

Google Cloud Made Easy

Now you know which Google Cloud tools should you learn first to face the challenges of any Google Cloud-based projects. However, there are a lot more tools and products that Google offers under the umbrella of Google Cloud.

Today or tomorrow, you need to become familiar with all Google Cloud tools to build strong command over Google Cloud operations. You can sign up for free online courses to speed up your IT learning goals.