Google Cloud has confirmed that its L1 blockchain project, called Google Cloud Universal Ledger (GCUL), is currently in the private testnet phase. Rich Widmann, who leads Web3 strategy at Google, said GCUL is the result of years of research and development in distributed ledger technology. Wildmann confirmed that GCUL incorporates Python-based smart contracts. He added that the L1 blockchain technology is being built as a neutral solution that will serve multiple financial institutions……..Continue reading….
By: Collins J. Okoth
Source: Cryptopolitan
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Google Cloud Platform provides infrastructure as a service, platform as a service, and serverless computing environments.In April 2008, Google announced App Engine, a platform for developing and hosting web applications in Google-managed data centers, which was the first cloud computing service from the company. The service became generally available in November 2011. Since the announcement of App Engine, Google added multiple cloud services to the platform.
Google Cloud Platform is a part of Google Cloud, which includes the Google Cloud Platform public cloud infrastructure, as well as Google Workspace (G Suite), enterprise versions of Android and ChromeOS, and application programming interfaces (APIs) for machine learning and enterprise mapping services. Since at least 2022, Google’s official materials have stated that “Google Cloud” is the new name for “Google Cloud Platform,” which may cause naming confusion.
Google Cloud Dataflow is a fully managed service for executing Apache Beam pipelines within the Google Cloud Platform ecosystem. Dataflow provides a fully managed service for executing Apache Beam pipelines, offering features like autoscaling, dynamic work rebalancing, and a managed execution environment. Dataflow is suitable for large-scale, continuous data processing jobs, and is one of the major components of Google’s big data architecture on the Google Cloud Platform.
Google Cloud Dataflow was announced in June, 2014 and released to the general public as an open beta in April, 2015. In January, 2016 Google donated the underlying SDK, the implementation of a local runner, and a set of IOs (data connectors) to access Google Cloud Platform data services to the Apache Software Foundation. The donated code formed the original basis for Apache Beam. In August 2022, there was an incident where user timers were broken for certain Dataflow streaming pipelines in multiple regions, which was later resolved.
Throughout 2023 and 2024, there have been various other updates and incidents affecting Google Cloud Dataflow, as documented in the release notes and service health history. Google Cloud Storage is an online file storage web service for storing and accessing data on Google Cloud Platform infrastructure.[1] The service combines the performance and scalability of Google’s cloud with advanced security and sharing capabilities.
It is an Infrastructure as a Service (IaaS), comparable to Amazon S3. Contrary to Google Drive and according to different service specifications, Google Cloud Storage appears to be more suitable for enterprises. User activation is resourced through the API Developer Console. Google Account holders must first access the service by logging in and then agreeing to the Terms of Service, followed by enabling a billing structure. Google Cloud Storage offers four storage classes, identical in throughput, latency and durability.
The four classes, Multi-Regional Storage, Regional Storage, Nearline Storage, and Coldline Storage, differ in their pricing, minimum storage durations, and availability.
- Interoperability – Google Cloud Storage is interoperable with other cloud storage tools and libraries that work with services such as Amazon S3 and Eucalyptus Systems.
- Consistency – Upload operations to Google Cloud Storage are atomic, providing strong read-after-write consistency for all upload operations.
- Access Control – Google Cloud Storage uses access control lists (ACLs) to manage object and bucket access. An ACL consists of one or more entries, each granting a specific permission to a scope. Permissions define what someone can do with an object or bucket (for example, READ or WRITE). Scopes define who the permission applies to. For example, a specific user or a group of users (such as Google account email addresses, Google Apps domain, public access, etc.)
- Resumable Uploads – Google Cloud Storage provides a resumable data transfer feature that allows users to resume upload operations after a communication failure has interrupted the flow of data.
Google Cloud Storage provides a durability guarantee of 99.999999999% (referred to as “11 nines”), primarily addressing data loss from hardware failures. However, this guarantee does not extend to losses resulting from human errors (such as accidental deletion), misconfigurations, third-party failures and subsequent data corruptions, natural disasters, force majeure events, or security breaches. Customers are responsible for monitoring SLA compliance and must submit claims for any unmet SLAs within a designated timeframe.
They should understand how deviations from SLAs are calculated, as these parameters may differ from those of other Google Cloud services. These requirements can impose a significant burden on customers. Additionally, SLA percentages and conditions can vary from those of other Google Cloud services. In cases of data loss due to hardware failure attributable to Google Cloud Platform, the company does not provide monetary compensation; instead, affected users may receive credits if they meet the eligibility criteria.
Google Cloud Datastore is a NoSQL database service provided by Google Cloud Platform. It is a fully managed database which can handle massive amounts of data and it is a part of the many services offered by Google Cloud Platform. It is designed to handle structured data (mostly document based like JSON format) and it also offers a high reliability and efficient platform to create scalable applications. Unlike traditional relational databases, this is a schema-less database concept.
This gives flexible data modeling and dynamic schema changes without downtime in its services that rely on this database. Google Cloud Datastore is platform used for data handling on mobile apps, web applications, and also the IoT systems. This is because of its key characteristics such as automatic scaling, strong consistency, and smooth integration with other Google Cloud services.
Google Cloud Datastore is built to handle software applications that are require high scalability, low-latency reads and writes, and automatic management of data across distributed systems. Google Cloud Datastore organizes data in entities and properties, where entities are grouped into kinds. This concept is similar to tables in relational databases, however since this is NoSQL database, it is without the schema constraints. Each entity in Datastore is uniquely identified by a key.
This key can have a custom user-defined identifier or it can be auto generated key by the system. Google Cloud Datastore offers an API and client libraries for different types of general purpose programming languages, like Python, Java, and Node.js. This API also has different release versions of these languages, so that Cloud Datastore can be integrated with both legacy and modern apps written in these languages. It also provides support for asynchronous operations. With this, developers can build non-blocking and highly responsive systems.
In the context of data consistency, Google Cloud Datastore provides strong consistency for single entity lookups and supports eventual consistency for queries across multiple entities. Users can use the database in Google Cloud Datastore using the Google Cloud Console, the gcloud command-line tool. They can also use client libraries for different programming languages. Based on the user need, they can choose to either use graphical interface or writing a code to interact with the database.
Data is organized into entities in Google Cloud Datastore. These are like individual records. These entities are grouped into kinds. This is just like tables in a traditional database. However, unlike relational databases, entities in the same kind do not have to follow a fixed structure (like a pre-defined schema). They can have different sets of properties. Each entity represents a structured set of properties. Properties are key-value pairs.
Examples of values can be strings, numbers, booleans, timestamps, arrays, and geographic points. The flexible nature of properties allows developers to model complex data structures without a rigid schema. GQL (Google Cloud Datastore Query Language) is a query language just like SQL and it is designed to interact with Google Cloud Datastore. GQL allows users to query the Datastore service using a statements just like SQL, however specifically designed to the NoSQL nature of this platform.
GQL provides ways to filter, order, and perform operations on Datastore entities without needing to write complex queries in the underlying datastore APIs. Unlike SQL, GQL is limited in terms of the types of joins[2] and relationships it can handle. However, it supports querying by properties, including equality and inequality, as well as range queries. Users can use GQL to query entities based on multiple conditions.
This makes GQL suitable for a wide range of use cases such as retrieving user data, product catalogs, and even updating the database. GQL also has support for ancestor queries. This lets users to get related entities based on their place in a hierarchy. This is very much needed for applications where we need to manage hierarchical data like a content management systems or data models that have parent-child relationships. Even though GQL can help in simplifying querying, it operates within the constraints of Datastore’s eventual consistency model.
As a default design, Cloud Datastore automatically indexes all properties in each entity to support faster querying. However, when dealing with complex queries that have multiple filters or sort orders, it is required to have composite indexes defined manually in an index.yaml file. Developers are required to review query plans and manage indexes carefully, otherwise unnecessary indexes can lead to increased write latency and storage costs. This is detrimental to performance, so it has to be avoided.
Datastore does not support joins, subqueries, or aggregation operations like those found in relational databases, such as MS SQL and MySQL. Because of this, application design often requires denormalization. It is a process of storing related data together within a single entity or using entity groups to maintain hierarchical relationships. Query filters are required to match existing indexes, and certain combinations of inequality and sort operations may require custom indexes.
Cloud Datastore supports ACID transactions for operations on entities within a single entity group. This enables safe updates to related data, such as parent and child entities. It is noted that single-entity lookups and ancestor queries are strongly consistent, however general queries across multiple entity groups offer eventual consistency.
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