#GoogleCloudSQL

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Everything as a Service To Reduce Costs, Risks & Complexity

What is Everything as a Service?

The phrase “everything as a service” (XaaS) refers to the expanding practice of providing a range of goods, equipment, and technological services via the internet. It’s basically a catch-all term for all the different “as-a-service” models that have surfaced in the field of cloud computing.

Everything as a Service examples

XaaS, or “Everything as a Service,” refers to the wide range of online software and services. Many services can be “X” in XaaS. Examples of common:

SaaS: Internet-delivered software without installation. Salesforce, Google Workspace, and Office 365.

Providing virtualized computer resources over the internet. These include AWS, Azure, and GCP.

Platform as a Service (PaaS): Provides equipment and software for application development online. Google App Engine and Azure App Services are examples.

Virtual desktops are available remotely with DaaS. Horizon   Cloud and Amazon WorkSpaces.

BaaS (Backend as a Service): Connects online and mobile app developers to cloud storage and APIs. Amazon Amplify and Firebase are examples.

DBaaS (Database as a Service) saves users from setting up and maintaining physical databases. Google Cloud SQL and Amazon RDS.

FaaS: A serverless computing service that lets customers execute code in response to events but not manage servers. AWS Lambda and Google  Cloud Functions.

STAaS: Provides internet-based storage. DropBox, Google Drive, and Amazon S3.

Network as a Service (NaaS): Virtualizes network services for scale and flexibility. SD-WAN is one.

By outsourcing parts of their IT infrastructure to third parties, XaaS helps companies cut expenses, scale up, and simplify. Flexibility allows users to pay for what they use, optimizing resources and costs.

IBM XaaS

Enterprises are requiring models that measure business outcomes instead of just IT results in order to spur rapid innovation. These businesses are under growing pressure to restructure their IT estates in order to cut costs, minimise risk, and simplify operations.

Everything as a Service (XaaS), which streamlines processes, lowers risk, and speeds up digital transformation, is emerging as a potential answer to these problems. By 2028, 80% of IT buyers will give priority to using Everything as a Service for critical workloads that need flexibility in order to maximise IT investment, enhance IT operations capabilities, and meet important sustainability KPIs, according to an IDC white paper sponsored by IBM.

Going forward, IBM saw three crucial observations that will keep influencing how firms develop in the upcoming years.

IT should be made simpler to improve business results and ROI

Enterprises are under a lot of pressure to modernise their old IT infrastructures. The applications that IBM is currently developing will be the ones that they must update in the future.

Businesses can include mission-critical apps into a contemporary hybrid environment using Everything as a Service options, especially for workloads and applications related to  artificial intelligence.

For instance, CrushBank and IBM collaborated to restructure IT assistance, optimising help desk processes and providing employees with enhanced data. As a result, resolution times were cut by 45%, and customer satisfaction significantly increased. According to CrushBank, consumers have expressed feedback of increased happiness and efficiency, enabling the company to spend more time with the people who matter most: their clients, thanks to Watsonx on IBM Cloud.

Rethink corporate strategies to promote quick innovation

 AI is radically changing the way that business is conducted. Conventional business models are finding it difficult to provide the agility needed in an AI-driven economy since they are frequently limited by their complexity and cost-intensive nature. Recent IDC research, funded by IBM, indicates that 78% of IT organisations consider Everything as a Service to be essential to their long-term plans.

Businesses recognise the advantages of using Everything as a Service to handle the risks and expenses associated with meeting this need for rapid innovation. This paradigm focusses on producing results for increased operational effectiveness and efficiency rather than just tools. By allowing XaaS providers to concentrate on safe, dependable, and expandable services, the model frees up IT departments to allocate their valuable resources to meeting customer demands.

Prepare for today in order to anticipate tomorrow

The transition to a Everything as a Service model aims to augment IT operations skills and achieve business goals more quickly and nimbly, in addition to optimising IT spending.

CTO David Tan of CrushBank demonstrated at Think how they helped customers innovate and use data wherever it is in a seamless way, enabling them to create a comprehensive plan that addresses each customer’s particular business needs. Enabling an easier, quicker, and more cost-effective way to use AI while lowering the risk and difficulty of maintaining intricate IT architectures is still crucial for businesses functioning in the data-driven world of today.

The trend towards Everything as a Service is noteworthy since it is a strategic solution with several advantages. XaaS ought to be the mainstay of every IT strategy, as it may lower operational risks and expenses and facilitate the quick adoption of cutting-edge technologies like artificial intelligence.

Businesses can now reap those benefits with IBM’s as-a-service offering. In addition to assisting clients in achieving their goals, IBM software and infrastructure capabilities work together to keep mission-critical workloads safe and compliant.

For instance, IBM Power Virtual Server is made to help top firms all over the world successfully go from on-premises servers to hybrid cloud infrastructures, giving executives greater visibility into their companies. With products like Watsonx Code Assistant for Java code or enterprise apps, the IBM team is also collaborating with their customers to modernise with  AI.

There is growing pressure on businesses to rebuild their legacy IT estates in order to minimise risk, expense, and complexity. With its ability to streamline processes, boost resilience, and quicken digital transformation, Everything as a Service is starting to emerge as the answer that can take on these problems head-on. IBM wants to support their customers wherever they are in their journey of change.

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[mandubian]

Tutorial for GoogleCloudSql with Play 1.2.3 & Siena 2.0.6 & Gae 1.6.0

After working on recent Play-GAE v1.6.0 and Play-Siena v2.0.6 providing Google Cloud Sql support, here is a tutorial showing how to develop a small for GAE using GoogleCloudSql.

The code is available on bitbucket!

This is a draft written quickly so might have remaining issues 

Have Fun with it anyway!

[govindhtech]

Cloud SQL for MySQL adds vector search, Gemini support, more

Using Cloud SQL for MySQL For a variety of applications, Cloud SQL for MySQL provides enterprises with the dependable performance, scalability, and dependability they want. Businesses like Nest and Chess.com are already using Cloud SQL for MySQL to power smart home devices and manage complicated game data. This robust foundation for data-driven solutions drives innovation and improves user experiences. Organizations are trying to use AI for their business objectives while utilizing the database that already supports their apps due to the increasing need for AI capabilities.

Google recently announced a number of new capabilities for Cloud SQL for MySQL, which is now available in preview and helps businesses use AI to power their databases and applications, in an effort to help them change their businesses. In order to aid you in creating cutting-edge generative AI apps and AI-assisted tools that streamline database administration and boost performance with Gemini, Google cloud now provide integrated support for vector embedding search. Now let’s explore these recent additions!

Create generative AI apps with Vector Search and connect them to MySQL Cloud Now that vector embeddings can be stored and searched for similarity in SQL for MySQL, you may include generative AI into your current applications. With the MySQL engine, it now offers approximate-nearest-neighbor (ANN) and K-nearest-neighbor (KNN) search between embeddings.

Integrating LangChain to produce vector embeddings Artificial intelligence systems can interact with your data more meaningfully if it is embedded as vectors. Complexities are preserved while information is maintained effectively when embedded as vectors. This allows AI programmes to compare distinct facts in an organized manner in order to identify commonalities.

A well-liked open-source framework for creating applications with large language models (LLMs) is called LangChain. In order to facilitate the data processing required to create vector embeddings and link it to your MySQL instance, the Cloud SQL team developed the Vector LangChain package. A vector storage, document loader, and chat message history are provided by the integration.

Google cloud offer an end-to-end example that shows how to create embeddings of data, like chat histories or huge documents, store the embeddings in MySQL, and search them, as well as a guide on using vector embeddings in MySQL with LangChain.

Use Vector Search to power generative AI applications Once your embedded data is saved on Cloud SQL for MySQL, you can calculate the vector distance between two embeddings to see how similar they are to one another. The computation of vector distances grows computationally costly and ultimately unfeasible as dimensions and data volume rise. When calculating the absolute distance is not an option, approximate-nearest-neighbor (ANN) search is used to find related vectors in a scalable, accurate manner.

Furthermore, Cloud SQL now uses Google’s ScaNN framework to power built-in ANN search of vector embeddings in MySQL as well as storage. Building generative AI applications becomes simpler as a result of the removal of the requirement for a separate vector-store database when using Cloud SQL for MySQL for data management.

Gemini for MySQL database management, debugging, and optimization Gemini can now be accessed at any point along the database trip. With Gemini in Databases, you can handle your database fleet’s whole lifespan, from migration to establishing the proper security and compliance measures to debugging performance problems. With a collection of MySQL-specific features, Cloud SQL for MySQL enables you to track and evaluate database-specific performance and identify issues before they have an influence on your applications.

Use Index Advisor to improve query efficiency AI-recommended indexes can help you optimize your MySQL workloads as well. Within the Query Insights dashboard, Index Advisor finds queries that add to database inefficiencies and suggests new indexes to make them more efficient. Index Advisor assists in detecting suboptimal queries and helping you detect performance problems before they have a detrimental effect on your company.

Index Advisor analyses your workload and suggests columns to add indexes to, along with an estimate of the index storage size and performance impact, to help you expedite your slow queries. It provides the precise queries required to build the suggested index, making the optimisation process more easier. Enable Index Advisor’s flag and check your query insights to get started.

Troubleshoot and avoid performance problems with Active Queries Real-time analysis of the ongoing queries on your instance is now available through the Query Insights panel. It offers a detailed analysis of the most popular queries that are presently executing on your database, along with an overview of the status of every connection. This report contains metrics, like the number of locked rows and transaction length, that are helpful in identifying expensive transactions. Active Query’s analysis helps you save time and effort troubleshooting by making clear which queries are running and how much they are costing.

You can end connections or inquiries as necessary in addition to performing active query analysis. The task of locating costly transactions and ending them in one easy move is streamlined by a centralised dashboard. With the query management capabilities that Active Queries brought, you can quickly pinpoint the cause of performance problems and see a high-level overview of your instance’s traffic to foresee future issues.

Use MySQL Recommender to track and enhance database health Keeping your MySQL instance’s parameters and flags at their ideal levels can be difficult, as there are many options to choose from. The challenge increases when there is a fluctuation in database traffic, leading to ever-changing database requirements. MySQL Recommender suggests adjusting configuration settings to boost security, safeguard data, and enhance speed. When appropriate, it also offers an explanation of its suggestion and other ways to maintain the instance’s health.

MySQL Recommender functions as a Gemini-powered MySQL expert by keeping an eye on numerous database health indicators and settings. It will identify, for instance, when you have a lot of open tables, are about to surpass the maximum number of open connections, or are executing a lot of joins without indexes. By keeping an eye on and preserving instance health, MySQL Recommender assists users in identifying and avoiding database problems.

Once you enable Gemini in Databases, the Recommender will be turned on automatically, allowing you to start fine-tuning your MySQL settings.

Cloud SQL for MySQL Pricing The cost of Cloud SQL for MySQL is contingent upon the dedicated-core or shared-core instance type that you select, as well as whether or not high availability is enabled. This is an explanation:

Shared-core instances: Their cost is determined by the type of instance (machine configuration) and their duration (seconds). The Google Cloud SQL documentation [cloud sql mysql cost] has the pricing information. Dedicated-core instances: These are an option if you require additional authority and control. The cost of them is determined by how many virtual CPUs and memory they contain. High Availability (HA): In addition to the base instance pricing, there is an additional HA pricing for instances (regional instances) configured for HA. For Cloud SQL, Google also provides a free tier so you may give it a try before committing to a subscription plan. Furthermore, new clients receive a $300 credit line for Cloud SQL.

Cloud SQL for MySQL supports automated and on demand backups Cloud SQL for MySQL enables both scheduled and spontaneous database backups. This allows you more leeway in developing a solid backup plan. Below is a brief summary of each:

Automated backups

Scheduled at a predetermined time slot by Google Cloud (usually with low impact on workload). Multiple backups can be kept for rollback reasons by configuring the retention time, which ranges from one day to a year. Ideal for automatically preserving backups on a regular basis. On-demand backups

Manually started anytime you require a quick database backup. Helpful for backing up data before important procedures or system modifications. Remain until you manually remove them or your instance is terminated. Excellent for making extra backups outside of the planned time frame. Recall that while automated backups adhere to the specified retention policy, on-demand backups are your responsibility to manage and remove.

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Hasura + BigQuery for Dynamic GraphQL APIs : Data Revolution

Using Hasura to power a GraphQL API over your BigQuery dataset

Google Cloud BigQuery is a crucial tool for building a data warehouse that can handle massive data sets with simplicity and scalability. Assume that you have established data pipelines to manage the datasets and that you have standardized on utilizing BigQuery.

Choosing the most effective way to make this data accessible to apps would be the next step. For this, APIs are often the best option. What you wanted to do was looking at a service that would make it simple for me to establish an API around my data sources, in this instance BigQuery.

They learn how to utilize Hasura, an open-source program, in this blog article. Hasura enabled me to build an API around my BigQuery dataset.

The simplicity of using an API to publish your domain data is the primary factor in favor of using Hasura. Hasura is compatible with several data sources, such as BigQuery, AlloyDB, and Google Cloud SQL. Through metadata setup, you have control over the model, relationships, validation, and permission logic.

Your GraphQL and REST APIs are generated by Hasura using this information. It offers a low-code data to API experience without sacrificing any of the speed, security, or flexibility that you want from your data API.

Although Hasura is free software, it is also available in fully managed versions on a number of cloud service providers, such as Google Cloud.

prerequisites

A Google Cloud Project is required. Please make a note of the project’s ID, since we will need it for subsequent usage in the Hasura settings.

Google Trends dataset – BigQuery dataset

Having a GraphQL API centered on our BigQuery dataset is our ultimate objective. So, a BigQuery dataset is what we must have built up. The Google Trends database that BigQuery’s Public Datasets initiative makes accessible is the one they have selected. This is an intriguing dataset that provides the top 25 overall or top 25 growing searches from Google Trends over the last 30 days, both domestically and internationally.

You copied the dataset and tables from the bigquery-public-data dataset and generated a sample dataset in BigQuery for their Google Cloud project called “google trends.”

The international top terms, which enable me to see trends across nations backed by the publicly accessible Google Trends information, is what you are interested in.

Account for services

Before we get to the Hasura setup, please be aware that creating a service account with the appropriate permissions is necessary for the interface between Hasura and Google Cloud. They will provide Hasura access to that service account so it can use BigQuery’s appropriate operations to set up and get the results.

In Google Cloud, creating a service account is simple and may be done using the Google Cloud Console.

You must export the service account using its credentials (JSON) file once it has been established. They will need that file in the next part, so please keep it secure.

Hasura arrangement

First, you have to register with Hasura. After signing it, choose New Project, Free Tier, and Google Cloud to host the Hasura API Layer, as seen below. Additionally, before clicking the Create Project button, you must choose which Google Cloud region to host the Hasura service in.

Configuring the data link

After the project is built, you must set up the Data Source that Hasura needs to setup and communicate with, as well as the communication between Hasura and Google Cloud.

Once the data connection has been established properly, you may choose which tables need monitoring. You can see that Hasura searched the metadata to locate the tables in the dataset by going to the Datasource settings.

After doing this, the table will be marked as monitored, and we can test the queries using the GraphQL Test UI.

Using the wonderful Explorer UI that the API tab offers, you can easily construct the GraphQL query.

This was very easy, and in a matter of minutes, my apps could be served by a GraphQL layer.

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