#bigtable

[govindhtech]

Introducing Google Axion Processors: A New Era for Cloud

Google Axion Processors

Arm Based CPU

At Google, they continuously push the limits of computers to investigate what can be done for big problems like global video distribution, information retrieval, and, of course, generative AI. Rethinking systems design in close cooperation with service developers is necessary to achieve this. Their large investment in bespoke silicon is the outcome of this rethinking. Google is excited to present the most recent iteration of this effort today: Google Axion Processors, Google’s first specially made Arm-based CPUs intended for data centers. Later this year, Axion which offers performance and energy efficiency that leads the industry will be made accessible to Google Cloud users.

Axion is only the most recent model of customised silicon from Google. Google’s first Video Coding Unit (VCU) increased video transcoding efficiency by 33x in 2018. Five generations of Tensor Processing Units have been launched since 2015. Google invested in “system on a chip” (SoC) designs and released the first of three generations of mobile Tensor processors in 2021 to boost bespoke computing.

General-purpose computing is and will continue to be a vital component of their customers’ workloads, even if Google investments in compute accelerators have revolutionised their capabilities. Extensive computation power is needed for analytics, information retrieval, and machine learning training and providing. The pace at which CPUs are being improved has slowed lately, which has affected customers and users who want to satisfy sustainability objectives, save infrastructure costs, and maximise performance. According to Amdahl’s Law, unless Google make the corresponding expenditures to stay up, general purpose compute will dominate the cost and restrict the capabilities of their infrastructure as accelerators continue to advance.

Google BigTable

In order to deliver instances with up to 30% better performance than the fastest general-purpose Arm-based instances currently available in the cloud, as well as up to 50% better performance and up to 60% better energy-efficiency than comparable current-generation x86-based instances, Axion processors combine Google’s silicon expertise with Arm’s highest performing CPU cores. For this reason, on current generation Arm-based servers, Google have already begun implementing Google services such as BigTable, Google Spanner, BigQuery, Blobstore, Pub/Sub, Google Earth Engine, and the YouTube Ads platform. Google also have plans to deploy and expand these services, along with others, on Axion shortly.

Superior effectiveness and performance, supported by Titanium

Axion processors, which are constructed around the Arm Neoverse V2 CPU, offer massive performance gains for a variety of general-purpose workloads, including media processing, web and app servers, containerised microservices, open-source databases, in-memory caches, data analytics engines, and more.

Titanium, a system of specially designed silicon microcontrollers and tiered scale-out offloads, provides the foundation for Axion. Platform functions like networking and security are handled by titanium offloads, giving Axion processors more capacity and enhanced performance for workloads from customers. Titanium also transfers I/O processing for storage to Hyperdisk, Google’s recently launched block storage solution that can be dynamically supplied in real time and decouples performance from instance size.

Titanium

A system of specially designed silicon security microcontrollers and tiered scale-out offloads that enhances the dependability, security, life cycle management, and performance of infrastructure.

Google-powered Titanium

Titanium is a free platform that supports Hyperdisk block storage, networking, the newest compute instance types (C3, A3, and H3), and more on Google Cloud.

Included in the system are:

Titan security microcontrollers are specially designed to provide Google Cloud’s infrastructure a hardware root of trust.

Titanium adaptor: specialised offload card that offers hardware acceleration for virtualization services; frees up resources for workloads by offloading processing from the host CPU

Titanium offload processors (TOPs) are silicon devices placed across the data centre that are used as a scalable and adaptable method of offloading network and I/O operations from the host CPU.

Enhanced functionality of the infrastructure

Titanium offloads computation from the host hardware to provide additional compute and memory resources for your applications.

Hyperdisk Extreme block storage allows for up to 500k IOPS per instance, which is the greatest among top hyperscalers.

200 Gbps or more of network bandwidth

Full line rate network encryption that offers security without compromising speed

Consistent performance comparable to bare metal for the most delicate workloads

Smooth management of the infrastructure life cycle

Infrastructure changes are made easier by Titanium’s modular hardware and software, which also provide offloading capabilities and workload continuity and security.

Advanced maintenance controls for the most critical workloads and seamless upgrades for the majority of workloads

It is possible to start remote infrastructure upgrades from any location.

The Titanium adaptor’s dedicated domains for networking and storage enable for the autonomous upkeep and upgrades of individual services, keeping them apart from the host’s burden.

“Building on Google’s high-performance Arm Neoverse V2 platform, Google’s announcement of the new Axion CPU represents a significant milestone in the delivery of custom silicon optimised for Google’s infrastructure.” The greatest experience for consumers using Arm is guaranteed by decades of ecosystem investment, Google’s continuous innovation, and its contributions to open-source software.”

Customers want to accomplish their sustainability objectives and operate more effectively, not only perform better. In comparison to five years ago, Google Cloud data centres are now 1.5 times more efficient than the industry average and provide 3 times more processing power with the same amount of electrical power. Google lofty objectives include running their campuses, offices, and data centres entirely on carbon-free energy sources around-the-clock and providing resources to assist with carbon emission reporting. Customers may optimise for even greater energy efficiency using Axion processors.

Axion: Interoperability and compatibility with out-of-the-box applications

Additionally, Google has a long history of supporting the Arm ecosystem. They worked closely with Arm and industry partners to optimize Android, Kubernetes, Tensorflow, and the Go language for the Arm architecture. Google also constructed and made them open-sourced.

Armv9 architecture

The standard Armv9 architecture and instruction set serve as the foundation for Axion. Google have made contributions to the SystemReady Virtual Environment (VE) standard, which is designed to ensure that Arm-based servers and virtual machines (VMs) can run common operating systems and software packages. This standard makes it easier for customers to deploy Arm workloads on Google Cloud with minimal to no code rewrites. Google is gaining access to an ecosystem of tens of thousands of cloud users that are already using Arm-native software from hundreds of ISVs and open-source projects and deploying workloads thanks to Google’s partnership.

Axion will be available to users across a variety of Google Cloud services, such as Cloud Batch, Dataproc, Dataflow, Google Compute Engine, and Google Kubernetes Engine. The Google Cloud Marketplace now offers Arm-compatible apps and solutions, and Google just released preview support for the Migrate to Virtual Machines service, which allows you to migrate Arm-based instances.

Read more on Govindhtech.com

[antstackinc]

AWS DynamoDB vs GCP BigTable| AntStack

Data is a precious resource in today’s fast-paced world, and it’s increasingly stored in the cloud for its benefits of accessibility, scalability, and, most importantly, security. As data volumes grow, individuals and businesses can easily expand their cloud storage without investing in new hardware or infrastructure. In the modern context, the answer to data storage often boils down to the cloud, but the choice between cloud services like AWS DynamoDB and GCP BigTable remains crucial.

[swiftnliftnewsandarticle]

How does Google store their data?

Google stores data using a highly sophisticated and distributed system that includes several key components:

1.Data Centers: Google operates large data centers around the world. These facilities house vast amounts of servers that store and process data. Every data center has sophisticated cooling and power management systems, as well as redundancy and efficiency in mind.

2. Distributed Storage: To guarantee data availability and dependability, Google employs distributed storage systems. Smaller pieces of data are dispersed among several servers and data centers. This method offers fault tolerance and improves performance.

3. File Systems: To efficiently manage enormous volumes of data, Google has created proprietary file systems including Google File System (GFS) and its successor, Colossus. Fault tolerance and high-throughput access are supported by these systems.

4. Database Systems: Depending on the use case, Google employs a variety of database technologies, such as Bigtable, Spanner, and Cloud SQL. Cloud SQL offers managed relational databases, Spanner delivers global transactional consistency, and Bigtable manages large-scale structured data.

5. Data Replication: To provide high availability and durability, data is copied across several servers and data centers. In the event of a hardware breakdown, this replication facilitates speedy recovery and helps prevent data loss.

6. Data Security: To safeguard data, Google uses a number of strong security measures, such as access limitations, encryption, and ongoing monitoring. Both in transit and at rest, data is encrypted.

By combining these technologies and practices, Google ensures that data is stored efficiently, securely, and reliably, supporting its vast array of services and applications.

[tumnikkeimatome]

NoSQLとSQLの融合：Google BigtableにGoogleSQLサポートが登場

GoogleがBigtableにSQLサポートを導入 Googleは、高速で柔軟性の高いNoSQLデータベースであるBigtableに、GoogleSQLサポートを導入しました。 この新機能により、ユーザーは馴染みのあるSQL構文を使用してBigtableのデータにクエリを実行できるようになりました。 GoogleSQLの特徴と利点 GoogleSQLは、SpannerやBigQueryなど、複数のGoogle Cloudサービスで使用されているクエリ言語です。 Bigtable用のGoogleSQLは、低レイテンシのアプリケーション開発に最適化されています。 Google Cloud コンソールでの利用 ユーザーはGoogle Cloud コンソールのBigtable…

[ujjinatd]

Google Cloud añade procesamiento de gráficos a Spanner y compatibilidad con SQL para Bigtable Olofson explicó que esta es la r... https://ujjina.com/google-cloud-anade-procesamiento-de-graficos-a-spanner-y-compatibilidad-con-sql-para-bigtable/?feed_id=713497&_unique_id=66ab8ff2e7666

[shilshatech]

Top Google Cloud Platform Development Services

Google Cloud Platform Development Services encompass a broad range of cloud computing services provided by Google, designed to enable developers to build, deploy, and manage applications on Google's highly scalable and reliable infrastructure. GCP offers an extensive suite of tools and services specifically designed to meet diverse development needs, ranging from computing, storage, and databases to machine learning, artificial intelligence, and the Internet of Things (IoT).

Core Components of GCP Development Services

Compute Services: GCP provides various computing options like Google Compute Engine (IaaS), Google Kubernetes Engine (GKE), App Engine (PaaS), and Cloud Functions (serverless computing). These services cater to different deployment scenarios and scalability requirements, ensuring developers have the right tools for their specific needs.

Storage and Database Services: GCP offers a comprehensive array of storage solutions, including Google Cloud Storage for unstructured data, Cloud SQL and Cloud Spanner for relational databases, and Bigtable for NoSQL databases. These services provide scalable, durable, and highly available storage options for any application.

Networking: GCP's networking services, such as Cloud Load Balancing, Cloud CDN, and Virtual Private Cloud (VPC), ensure secure, efficient, and reliable connectivity and data transfer. These tools help optimize performance and security for applications hosted on GCP.

Big Data and Analytics: Tools like BigQuery, Cloud Dataflow, and Dataproc facilitate large-scale data processing, analysis, and machine learning. These services empower businesses to derive actionable insights from their data, driving informed decision-making and innovation.

AI and Machine Learning: GCP provides advanced AI and ML services such as TensorFlow, Cloud AI, and AutoML, enabling developers to build, train, and deploy sophisticated machine learning models with ease.

Security: GCP includes robust security features like Identity and Access Management (IAM), Cloud Security Command Center, and encryption at rest and in transit. These tools help protect data and applications from unauthorized access and potential threats.

Latest Tools Used in Google Cloud Platform Development Services

Anthos: Anthos is a hybrid and multi-cloud platform that allows developers to build and manage applications consistently across on-premises and cloud environments. It provides a unified platform for managing clusters and services, enabling seamless application deployment and management.

Cloud Run: Cloud Run is a fully managed serverless platform that allows developers to run containers directly on GCP without managing the underlying infrastructure. It supports any containerized application, making it easy to deploy and scale services.

Firestore: Firestore is a NoSQL document database that simplifies the development of serverless applications. It offers real-time synchronization, offline support, and seamless integration with other GCP services.

Cloud Build: Cloud Build is a continuous integration and continuous delivery (CI/CD) tool that automates the building, testing, and deployment of applications. It ensures faster, more reliable software releases by streamlining the development workflow.

Vertex AI: Vertex AI is a managed machine learning platform that provides the tools and infrastructure necessary to build, deploy, and scale AI models efficiently. It integrates seamlessly with other GCP services, making it a powerful tool for AI development.

Cloud Functions: Cloud Functions is a serverless execution environment that allows developers to run code in response to events without provisioning or managing servers. It supports various triggers, including HTTP requests, Pub/Sub messages, and database changes.

Importance of Google Cloud Platform Development Services for Secure Data and Maintenance

Enhanced Security: GCP employs advanced security measures, including encryption at rest and in transit, identity management, and robust access controls. These features ensure that data is protected against unauthorized access and breaches, making GCP a secure choice for sensitive data.

Compliance and Certifications: GCP complies with various industry standards and regulations, such as GDPR, HIPAA, and ISO/IEC 27001. This compliance provides businesses with the assurance that their data handling practices meet stringent legal requirements.

Reliability and Availability: GCP's global infrastructure and redundant data centers ensure high availability and reliability. Services like Cloud Load Balancing and auto-scaling maintain performance and uptime even during traffic spikes, ensuring continuous availability of applications.

Data Management: GCP offers a range of tools for efficient data management, including Cloud Storage, BigQuery, and Dataflow. These services enable businesses to store, process, and analyze vast amounts of data seamlessly, driving insights and innovation.

Disaster Recovery: GCP provides comprehensive disaster recovery solutions, including automated backups, data replication, and recovery testing. These features minimize data loss and downtime during unexpected events, ensuring business continuity.

Why Shilsha Technologies is the Best Company for Google Cloud Platform Development Services in India

Expertise and Experience: Shilsha Technologies boasts a team of certified GCP experts with extensive experience in developing and managing cloud solutions. Their deep understanding of GCP ensures that clients receive top-notch services customized to your requirements.

Comprehensive Services: From cloud migration and application development to data analytics and AI/ML solutions, Shilsha Technologies offers a full spectrum of GCP services. This makes them a one-stop solution for all cloud development needs.

Customer-Centric Approach: Shilsha Technologies emphasizes a customer-first approach, ensuring that every project aligns with the client's business goals and delivers measurable value. It's their commitment to customer satisfaction that sets them apart from the competition.

Innovative Solutions: By leveraging the latest GCP tools and technologies, Shilsha Technologies delivers innovative and scalable solutions that drive business growth and operational efficiency.

Excellent Portfolio: With an excellent portfolio of successful projects across various industries, Shilsha Technologies has demonstrated its ability to deliver high-quality GCP solutions that meet and exceed client expectations.

How to Hire a Developer in India from Shilsha Technologies

Initial Consultation: Contact Shilsha Technologies through their website or customer service to discuss your project requirements and objectives. An initial consultation will help determine the scope of the project and the expertise needed.

Proposal and Agreement: Based on the consultation, Shilsha Technologies will provide a detailed proposal outlining the project plan, timeline, and cost. Contracts are signed once they have been agreed upon.

Team Allocation: Shilsha Technologies will assign a dedicated team of GCP developers and specialists customized to your project requirements. The team will include project managers, developers, and QA experts to ensure seamless project execution.

Project Kickoff: The project begins with a kickoff meeting to align the team with your goals and establish communication protocols. Regular updates and progress reports keep you informed throughout the development process.

Ongoing Support: After the project is completed, Shilsha Technologies offers ongoing support and maintenance services to ensure the continued success and optimal performance of your GCP solutions.

Google Cloud Platform Development Services provide robust, secure, and scalable cloud solutions, and Shilsha Technologies stands out as the premier Google Cloud Platform Development Company in India. By choosing Shilsha Technologies, businesses can harness the full potential of GCP to drive innovation and growth. So, if you're looking to hire a developer in India, Shilsha Technologies should be your top choice.

Source file

Reference: https://hirefulltimedeveloper.blogspot.com/2024/07/top-google-cloud-platform-development.html

[nagataka-lifelog]

2024-06-13 / connecting the dots

最近はどこへいってもLLM LLMの大合唱だけれども、かくいう自分もなんだかんだマルチモーダルな人になっていて vision-language pre-training で最近はメシを食っている。ふと考えてみると、今の自分っていうのは色んな偶然に左右されているなと思う。好むと好まざるとにかかわらず（村上春樹風味）。そんなことを考えていた時に頭に思い出された諸々の殴り書き。

---

「アプリケーション開発がやりたい」と面接で主張していたにも関わらず、新卒で入社した会社ではインフラの保守運用色が強いチームに配属になったところからスタートした自分のキャリア。けどこれがきっかけでサーバやネットワークの事を勉強することができて（まぁ当時は不満だったけれども）、それがのちにクラウドベンダーへ転職することにつながった。そのクラウドベンダーではITシステムの下から上まで、幅広い知識が求められ、前職で経験したITインフラ関連の知識だけでなく、学生時代にやっていたプログラマのアルバイトで経験したり就職してから終業後や週末にやっていたwebアプリケーション開発の知識も総動員した。

しばらくは幅広く色んな分野を引き続き勉強していくだけで手一杯だったけれど、ある程度経ってからは自分独自の領域を作らないとと焦る中で、当時チームに専門家が少なかった（一人いたのだが、自分が入社して程なく米国本社へと転籍して我々からは遠くにいってしまった）ビッグデータ関連の技術に目をつけて勉強を始める。当時、会社のCTOがブログで論文読みのシリーズを書いており、それに感化された自分もMapReduceやBigTableなどビッグデータ関連の論文を読んでみようと思いつく。また、それらを学ぶ中で機械学習という分野に出会うことになる（自分が卒業した大学の情報科学科には当時、機械学習の授業は無く、シンボリックないわゆる論理型人工知能しか知らなかった。）遅かれ早かれ、これだけブームになって現在に至るわけで機械学習には手を出していたと思うが、この時点でビッグデータ関連の技術に自分のフォーカスを持っていっていなければその後自分がどうなっていたのかは全くわからない。機械学習に非常にワクワクさせられ、かつ久しぶりに論文を読む中で楽しさを感じていた自分はいつからか博士課程に行って研究をしてみたいと思うに至る。

幸いカリフォルニアのとある大学院に受け入れてもらえることが決まり、2017年に渡米（そういえば、このブログを始めたのは確か「これからの貴重な留学生活を全て記録してやろう」と思い立ってのことだった。。程なくして更新は滞ってしまったが。。）DQNやAlphaGoに興奮していた自分は自然に強化学習をテーマとして選ぶ。入学のほんの一年半か二年ほど前からほぼ独学でスタートした機械学習だったので博士課程では散々苦労したけれどもなんとか学位を取得することができ（もちろんここで簡潔に書き切れるようなボリュームではない）、卒業後はとあるBig Techの画像検索のチームにサイエンティストとして就職する。

思えばこのチームに入ったのも本当に偶然だった。最初は、前職の同僚のコネクションを頼ってリファラルをもらい最初のインターンの機会をなんとか掴んだところから始まった。最初にインターンをしたチームの仕事は人事関連 x 機械学習（今だとHR Techとか言うのだろうか）で、正直自分の興味としてはハテナだったのだが、とにかく一度やってみないことにはわからないだろうという性格なので飛び込んでみることに。米国本社でのインターンは非常に楽しく充実した経験になり、幸いリターンオファーまで頂くことができて成功だったが、やっぱり分野的に自分の興味関心とは少しズレている感じがありオファーは断り次の夏は研究室にこもろうと決意する。という事で、リターンオファーを断ったのでインターンのことは全く考えていなかったのだが翌年の春に突然リクルーターからメールが来て、「Visual Searchのチームに興味はないか？」とのこと。「この夏は研究室にこもってバリバリ研究するぜ！」と燃えていたはずなのに、少し考えた後にはもう「Visual Search！面白そう！！」となってYESと返信していた自分（研究の進捗が芳しくなくてちょっと逃避したかったのもあるかな 笑。）面接は1st phone screen的な非常にカジュアルな会話だけでなぜか終わり、ラッキーなことにオファーをゲット。その年と翌年の二度のインターンを経てフルタイムオファーを貰うという流れにつながる。この時の面接官でかつインターンの際のメンター・マネージャーだった同僚には、なんで自分に声をかけてきたのか、それともあれはリクルーターが偶然自分のレジュメをプールの中からピックアップして彼に共有しただけのことなのか聞けてはいない。インターンリターンオファーは半年前に断っていたのに、なぜ自分に再度連絡が来たのかはわからないし、特に知ろうともしていないのだけど。

長くなったが、そんなこんなで入社した画像検索のチーム。強化学習を博士課程で研究していた自分にとってはそれなりに未知の分野だったので、必死でキャッチアップを試みる日々が始まった。入社からしばらく経って、一部の同僚たちが画像検索に使うモデルのマルチモーダル化に取り組んでいることに気づく。コンピュータビジョンのキャッチアップだけでも手一杯なのに自然言語のことまではちょっと手が回らないなぁ、と横目で見ていたのだが、入社から半年ほどした頃になんと自分にも関連したプロジェクトの話が回ってきて、（「仕方がなく」と言うとアレだけれど、実際「仕方がない」と覚悟を決めて）必死にキャッチアップが始まる。そうこうしている中で、ChatGPTの登場を機に世の中が猫も杓子もLLMという状況に。

ようやく冒頭の内容に戻るが、ふと自分のこれまでと現状を眺めてみると、実験で使う環境の整備にはこれまで培ったITインフラ・クラウドの知識が動員され、データの処理にはそれらの知識に加えてPySparkなどのビッグデータ関連の知識を用い、そして実験結果のビジュアライズにはwebアプリ開発の知識がそこそこ役に立っている。そしてコアの部分としては機械学習の知識、特にマルチモーダルなモデルのトレーニングから、さらにここにきてRLHFの登場により自分の強化学習のバックグラウンドまで役にたつという流れができている。こうして見てみると、今までやってきたことが集まって今の自分を構成しているんだなと本当に思う。Steve Jobsが "connecting the dots" という話をしていたけれども、確かにこれは振り返ってみるととても自然に思えるけれども自分が前に進んでいる時にそれらを繋げようと意識していたかというと意識していないことの方が多いし、その時は偶然目の前に現れた機会に自分の持てる道具でただただ立ち向かっているだけで必死になっていてそんなことは考えていないことが往往にして多い。こうしてそれなりに見通しの良い場所に立っている幸運に感謝し��つ、また一年後なのか三年後なのか五年後なのかわからないけれど後ろを振り返った時に、どんな自明なつながりを発見することになるのか今から楽しみだ。（こうやって振り返る際には喉元過ぎればなんとやらで、実態は毎日毎日キャッチアップと成果を出すのにひぃひぃ言ってるのの繰り返しなんだけどね）

[yourusatoday]

Analysis of MarkLogic Corp Competitors

Introduction to MarkLogic Corp

MarkLogic Corp is a leading provider of data integration and database solutions, empowering organizations to manage and leverage their data effectively. As a key player in the data management industry, MarkLogic faces competition from several companies offering similar solutions. This analysis provides insights into MarkLogic's main competitors and their offerings.

Competitors of MarkLogic Corp

1. MongoDB, Inc.

MongoDB, Inc. is a prominent competitor of MarkLogic, offering a popular document-oriented NoSQL database solution. MongoDB's flexible data model and scalability make it a preferred choice for many organizations dealing with large volumes of unstructured data.

Key Features:

Document-oriented database with JSON-like documents.

Scalable architecture suitable for distributed environments.

Comprehensive query language and indexing capabilities.

2. Oracle Corporation

Oracle Corporation is a global leader in database management systems, offering a wide range of solutions, including Oracle Database. Oracle's database solutions provide robust relational database management capabilities, making them suitable for enterprise-scale applications.

Key Features:

Powerful relational database management system (RDBMS) with ACID compliance.

Advanced security features and data protection capabilities.

Support for various data types and integration with other Oracle products.

3. Microsoft Corporation

Microsoft Corporation offers SQL Server, a comprehensive relational database management system, competing with MarkLogic in the enterprise database market. SQL Server provides extensive features for data management, analytics, and business intelligence.

Key Features:

Integrated suite of data management tools for relational databases.

Advanced analytics and reporting capabilities with SQL Server Analysis Services (SSAS) and Power BI.

Seamless integration with Microsoft Azure cloud platform for scalability and flexibility.

4. Amazon Web Services, Inc. (AWS)

Amazon Web Services offers Amazon DynamoDB, a fully managed NoSQL database service, as a competitor to MarkLogic. DynamoDB is known for its scalability, high performance, and seamless integration with other AWS services.

Key Features:

Fully managed NoSQL database with automatic scaling.

Built-in security features and encryption at rest and in transit.

Integration with AWS ecosystem for seamless application development and deployment.

5. Google LLC

Google LLC provides Google Cloud Bigtable, a fully managed, scalable NoSQL database service, competing with MarkLogic in the cloud database market. Bigtable is designed for large-scale, high-throughput workloads.

Key Features:

Distributed NoSQL database for real-time analytics and data processing.

High scalability and performance for handling massive datasets.

Integration with Google Cloud Platform services for seamless application development.

Competitive Analysis

Strengths of Competitors

MongoDB: Flexible data model and scalability.

Oracle: Robust RDBMS with advanced security features.

Microsoft: Integrated suite of data management tools and cloud integration.

AWS: Fully managed NoSQL database service with seamless scalability.

Google: High scalability and performance for real-time analytics.

MarkLogic's Competitive Advantages

Advanced semantics and indexing capabilities for complex data.

Support for structured, semi-structured, and unstructured data.

Flexibility to deploy on-premises or in the cloud.

Strong focus on data security and compliance.

Conclusion

MarkLogic Corp faces competition from several key players in the database management industry, each offering unique solutions tailored to different use cases. While competitors like MongoDB, Oracle, Microsoft, AWS, and Google offer strong products, MarkLogic stands out with its advanced semantics, flexible deployment options, and robust security features. Understanding the strengths and offerings of competitors is essential for MarkLogic to continue innovating and providing value to its customers in the ever-evolving data management landscape.

[khushnumaidrishi]

Top 5 Big Data Databases in 2024: Features, Benefits, Pricing

In 2024, the top 5 Big Data databases are Apache Cassandra, MongoDB, Amazon DynamoDB, Google Bigtable, and Apache HBase. Apache Cassandra offers high availability and fault tolerance with a decentralized architecture. MongoDB provides flexibility and scalability for handling unstructured data. Amazon DynamoDB offers seamless scalability and low-latency performance with managed infrastructure. Google Bigtable excels in handling massive datasets with low-latency access. Apache HBase provides consistent read and write performance for large-scale applications. Pricing varies based on usage and additional services, with options for both pay-as-you-go and subscription models.

[govindhtech]

Using change streams to expand your Bigtable architecture

Change Streams in Bigtable architecture

As part of their data workflow, engineers use Bigtable to store enormous amounts of transactional and analytical data. The introduction of Bigtable change streams, which will improve these data workflows for event-based architectures and offline processing, is something we are enthusiastic about. We will discuss the new feature and a few examples of apps that use change streams in this article.

Alternate streams

Real-time modifications made to Bigtable tables are captured and output by change streams. You can access the stream using the Data API, but we advise utilizing the Dataflow connector because it provides an abstraction over the complexity of processing partitions using the Apache Beam SDK and the change streams Data API. Dataflow is a managed service that will help with the scalability and dependability of stream data processing by provisioning and managing resources.

Instead of worrying about specific Bigtable specifics like appropriately managing partitions over time and other non-functional needs, the connector lets you concentrate on the business logic.

Change streams on your table can be enabled using the client libraries, gcloud CLI, Terraform, or the Console. Following that, you may use our change stream quickstart to begin developing.

Illustrative architectures

You can track changes to data in real time and respond swiftly thanks to change streams. By using the data in new ways, you can more quickly automate operations based on data updates or add new features to your program. Here are a few examples of Bigtable-based application architectures that employ change streams.

Data enrichment using contemporary AI

AI-related new APIs are being developed quickly and have the potential to greatly enhance your application data. You may improve data for your clients by using APIs for audio, graphics, translation, and other services. Bigtable change streams provide a direct route for enhancing new data as it is added.

Using pre-built models from Vertex AI, we are transcribing and summarizing voice messages in this instance. Bigtable can be used to store the raw audio file in bytes, and change streams are used to start AI audio processing whenever a new message is introduced. The Speech API will be used by a Dataflow pipeline to obtain a transcription of the message, and the PaLM API will be used to condense that transcription. These can be entered into Bigtable so that users can access them and send messages using their preferred channel.

Search in full-text and autocomplete

There are numerous applications, ranging from online stores to streaming media platforms, that frequently make use of full-text search and autocomplete. In this case, a music platform is giving its music collection full-text search capabilities by indexing album names, song titles, and artists in Elasticsearch.

A pipeline in Dataflow records the changes as new music are added. The data that has to be indexed will be extracted, then written to Elasticsearch. This keeps the index current, and users can utilize a search engine hosted on Cloud Functions to query it.

Alerts based on events

An important tool for application development is the processing of events and the real-time notification of customers. Your architecture can be changed to accommodate pop-ups, push notifications, emails, SMS, etc. Here is an illustration of what a logistics and shipping firm might perform.

Millions of goods are constantly roaming the globe thanks to logistics and shipping firms. In order for each box to proceed to the following location, they need to maintain track of where it is as it arrives at each new distribution center. Customers have the option to sign up for email or text updates regarding the status of their packages, which may be useful if they are waiting for a new pair of shoes or if a hospital needs to know when their next shipment of gloves is coming.

This event-based architecture complements Bigtable change streams very well. Data on the packages leaving shipping hubs and being written to Bigtable is available in real-time. Our Dataflow alerting solution, which uses SendGrid and Twilio APIs for simple email and text notifications, captures the change stream.

Analytics in real time

Any application that makes use of Bigtable will probably have a ton of data. Change streams, as opposed to huge, uncommon batch processes, let you change metrics in small increments as the data comes in, opening up real-time analytics use cases. To do aggregation queries on the data in the window and write the results to another table for analytics and dashboarding, you may design a windowing scheme for regular intervals.

This architecture demonstrates a business that provides a SaaS platform for online retail and wishes to provide to its clients the performance indicators for their online shops, such as the number of visits, conversion rates, abandoned shopping carts, and most popular items. They upload the data to Bigtable, aggregate it every five minutes based on the criteria they want their users to utilize for data slicing and dicing, and then write the results to an analytics table. They can take data from the analytics table to build real-time dashboards using tools like D3.js, giving them better understanding of their consumers.

Next procedures

You are now familiar with fresh uses for Bigtable in event-driven architectures and how to use change streams to manage your data for analytics.

[teknolojihaber]

Google, veri merkezlerine yönelik ilk Arm tabanlı CPU'su Axion'u duyurdu

Yeni çipler, Google'ın Amazon'un veri merkezlerine güç sağlayan Arm çiplerinin rakibi olacak. Google Cloud Next 2024 başladı ve şirket, etkinliğe yeni Axion işlemcisi de dahil olmak üzere bazı büyük duyurularla başlıyor. Bu, Arm'ın Neoverse V2 CPU'su kullanılarak tasarlanmış, Google'ın veri merkezleri için özel olarak oluşturulmuş ilk Arm tabanlı CPU'sudur. Google'a göre Axion, buluttaki en hızlı genel amaçlı Arm tabanlı araçlarından yüzde 30, en yeni, karşılaştırılabilir x86 tabanlı VM'lerden ise yüzde 50 daha iyi performans gösteriyor. Ayrıca aynı x86 tabanlı VM'lere göre enerji açısından yüzde 60 daha verimli olduğunu iddia ediyorlar. Google, Axion'u BigTable ve Google Earth Engine gibi hizmetlerde zaten kullanıyor ve gelecekte daha fazlasını da kapsayacak şekilde genişliyor. Axion'un piyasaya sürülmesi Google'ı, veri merkezleri için Arm tabanlı CPU'lar alanında lider olan Amazon ile rekabete sokabilir. Şirketin bulut işletmesi Amazon Web Services (AWS), Graviton işlemcisini 2018'de piyasaya sürdü ve sonraki iki yıl içinde ikinci ve üçüncü versiyonları piyasaya sürdü. Diğer çip geliştiricisi NVIDIA, 2021'de Grace adlı veri merkezleri için ilk Arm tabanlı CPU'sunu piyasaya sürdü ve Ampere gibi şirketler de bu alanda kazanç elde ediyor. Arm tabanlı işlemciler genellikle daha düşük maliyetli ve enerji açısından daha verimli bir seçenektir. Wall Street Journal'a göre Google'ın duyurusu, Arms CEO'su Rene Haas'ın yapay zeka modellerinin enerji kullanımına ilişkin bir uyarı yayınlamasının hemen ardından geldi . ChatGPT gibi modelleri elektrik ihtiyaçları açısından "doyumsuz" olarak nitelendirdi. Haas, "Ne kadar çok bilgi toplarlarsa o kadar akıllı olurlar, ancak daha akıllı olmak için ne kadar çok bilgi toplarlarsa o kadar fazla güç harcarlar" dedi. kaynak:https://www.engadget.com Read the full article

[azuretrainingin]

Google Cloud Platform (GCP) Data Types

Google Cloud Platform (GCP) Data Types and Key Features:

Google Cloud Platform (GCP) offers a comprehensive suite of data services tailored to meet the diverse needs of modern businesses. From storage and databases to big data processing and analytics, GCP provides a wide range of data types and key features to empower organizations to store, manage, process, and analyze their data efficiently and effectively. In this guide, we'll explore the various data types offered by GCP along with their key features, benefits, and use cases.

1. Structured Data:

Structured data refers to data that is organized in a specific format, typically with a well-defined schema. GCP offers several services for managing structured data:

Google Cloud SQL:

Key Features:

Fully managed relational database service.

Supports MySQL and PostgreSQL databases.

Automated backups, replication, and failover.

Seamless integration with other GCP services.

Benefits:

Simplifies database management tasks, such as provisioning, scaling, and maintenance.

Provides high availability and reliability with built-in replication and failover capabilities.

Enables seamless migration of existing MySQL and PostgreSQL workloads to the cloud.

Google Cloud Spanner:

Key Features:

Globally distributed, horizontally scalable relational database.

Strong consistency and ACID transactions across regions.

Automatic scaling and maintenance with no downtime.

Integrated security features, including encryption at rest and in transit.

Benefits:

Enables global-scale applications with low latency and high availability.

Supports mission-critical workloads that require strong consistency and ACID transactions.

Simplifies database management with automated scaling and maintenance.

2. Unstructured Data:

Unstructured data refers to data that does not have a predefined data model or schema, making it more challenging to analyze using traditional database techniques. GCP offers several services for managing unstructured data:

Google Cloud Storage:

Key Features:

Object storage service for storing and retrieving unstructured data.

Scalable, durable, and highly available storage with multiple redundancy options.

Integration with other GCP services, such as BigQuery and AI Platform.

Advanced security features, including encryption and access controls.

Benefits:

Provides cost-effective storage for a wide range of unstructured data types, including images, videos, and documents.

Offers seamless integration with other GCP services for data processing, analytics, and machine learning.

Ensures data durability and availability with built-in redundancy and replication.

Google Cloud Bigtable:

Key Features:

Fully managed NoSQL database service for real-time analytics and high-throughput applications.

Designed for massive scalability and low-latency data access.

Integrates with popular big data and analytics tools, such as Hadoop and Spark.

Automatic scaling and performance optimization based on workload patterns.

Benefits:

Enables real-time analytics and data processing with low-latency access to large-scale datasets.

Supports high-throughput applications that require massive scalability and fast data ingestion.

Simplifies database management with automated scaling and performance optimization.

3. Semi-Structured Data:

Semi-structured data refers to data that does not conform to a rigid schema but has some structure, such as JSON or XML documents. GCP offers services for managing semi-structured data:

Google Cloud Firestore:

Key Features:

Fully managed NoSQL document database for mobile, web, and server applications.

Real-time data synchronization and offline support for mobile apps.

Automatic scaling and sharding for high availability and performance.

Integration with Firebase and other GCP services for building modern applications.

Benefits:

Enables developers to build responsive, scalable applications with real-time data synchronization and offline support.

Provides automatic scaling and sharding to handle growing workloads and ensure high availability.

Integrates seamlessly with other GCP services, such as Firebase Authentication and Cloud Functions.

4. Time-Series Data:

Time-series data refers to data that is collected and recorded over time, typically with a timestamp associated with each data point. GCP offers services for managing time-series data:

Google Cloud BigQuery:

Key Features:

Fully managed data warehouse and analytics platform.

Scalable, serverless architecture for querying and analyzing large datasets.

Support for standard SQL queries and machine learning models.

Integration with popular business intelligence tools and data visualization platforms.

Benefits:

Enables ad-hoc analysis and interactive querying of large-scale datasets with high performance and scalability.

Provides a serverless architecture that eliminates the need for infrastructure provisioning and management.

Integrates seamlessly with popular BI tools and visualization platforms for generating insights and reports.

5. Graph Data:

Graph data refers to data that is modeled as a graph, consisting of nodes and edges representing entities and relationships between them. GCP offers services for managing graph data:

Google Cloud Graph Database:

Key Features:

Fully managed graph database service for building and querying graph data models.

Supports property graphs and RDF graphs for representing structured and semi-structured data.

Integration with popular graph query languages, such as Cypher and SPARQL.

Automatic scaling and replication for high availability and performance.

Benefits:

Enables developers to build and query complex graph data models with ease using familiar query languages.

Provides automatic scaling and replication to handle growing workloads and ensure high availability.

Integrates seamlessly with other GCP services for data processing, analytics, and machine learning.

Click Here For More Information To Get Into The Our Services

[edchart]

Google Cloud Bigtable Developer Certification Exam Free Test - By EDCHART

Google Cloud Bigtable Developer Certification Description

The Google Cloud Bigtable Developer Certification is designed to validate the expertise of professionals in developing applications using Google Cloud Bigtable, a fully managed, highly scalable NoSQL database service. The certification assesses proficiency in leveraging Bigtable's capabilities to handle massive amounts of data with low-latency performance. Aspiring candidates are evaluated on their ability to design schema, optimize query performance, and integrate Bigtable with other Google Cloud services.

Scopes of Google Cloud Bigtable Developer Certification Career

A Google Cloud Bigtable Developer Certification opens doors to a dynamic career in cloud-based data management and analytics. Certified professionals are sought after by organizations looking to harness the power of Bigtable for real-time data processing, analytics, and IoT applications. The scope extends across industries such as finance, e-commerce, healthcare, and more, where handling large-scale data efficiently is crucial.

Pros of Using Google Cloud Bigtable Developer Certification:

In-Demand Skills: The Google Cloud Bigtable Developer Certification showcases proficiency in a sought-after skill set, making certified individuals attractive to employers.

Cloud Expertise: It demonstrates expertise in leveraging Google Cloud services for optimal data management and processing.

Career Advancement: Opens opportunities for roles such as Google Cloud Bigtable Developer Certification, Cloud Engineer, and Data Engineer.

Cons of Using Google Cloud Bigtable Developer Certification:

Specialized Knowledge: The Google Cloud Bigtable Developer Certification focuses on a specific technology, which may limit its applicability in broader IT roles.

Evolution of Technology: As technology evolves, ongoing learning may be required to stay current with the latest developments in Google Cloud services.

Prominent Companies Built with Google Cloud Bigtable Developer Certification:

Prominent organizations leveraging Google Cloud Bigtable Developer Certification include Spotify, eBay, and The New York Times, showcasing the certification's applicability in diverse industries for handling large-scale, real-time data.

Features of Google Cloud Bigtable Developer Certification

The Google Cloud Bigtable Developer Certification emphasizes key features such as schema design, query optimization, and integration with other Google Cloud services. Practical skills in building scalable, high-performance applications using Bigtable are central to the certification's curriculum.

Benefits and Advantages of Google Cloud Bigtable Developer Certification

Google Cloud Bigtable Developer Certification offers a competitive edge by validating skills in a high-demand area, leading to increased career opportunities, professional growth, and the ability to contribute effectively to projects involving large-scale data processing.

Why Should One Take Google Cloud Bigtable Developer Certification?

The Google Cloud Bigtable Developer Certification is essential for individuals seeking recognition for their proficiency in designing and implementing solutions with Google Cloud Bigtable. It not only validates skills but also provides a structured learning path for mastering the intricacies of this powerful NoSQL database service.

Who Will Benefit from Taking Google Cloud Bigtable Developer Certification?

Google Cloud Bigtable Developer Certification, Database developers, cloud engineers, and data engineers looking to enhance their skills in cloud-based data management and processing will benefit from the certification. It is suitable for professionals working or aspiring to work with large-scale, real-time data applications.

Skills Required for Google Cloud Bigtable Developer Certification:

Proficiency in NoSQL databases, Google Cloud Bigtable Developer Certification, cloud computing, and programming languages like Java and Python is beneficial. Practical experience in designing and optimizing schemas for large datasets is also essential.

Top Search Keywords for Google Cloud Bigtable Developer Certification:

Google Cloud Bigtable certification

Bigtable Developer exam

Cloud data management skills

Real-time data processing certification

NoSQL database expertise

Google Cloud Professional Certification

Cloud-based application development

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Cloud data engineering skills.

[juneboku]

むかし You Are Not Google と言って大げさな big data スタックに水を差した人がいたけ。プロセスや組織デザインにも同じところがあるのではないか。つまり、でかくなるまでは洗練されなくてよい。BigTable も Dynamo もいらねーんだよ Postgres と Redis で文句あっか・・・みたいな。

出戻り – Spinach Forest

[gu4]

RDB以外のものに書く これは本当の所はトランザクションが必要無い場合に採用可能です。この場合は各種分散データストアの導入を検討することになります。弊社ではKafkaやCassandraを利用している箇所がありますが、レプリカ書き込みを含めて秒間100万件ぐらいの書き込みが発生しています。例えばCassandraは読み込みよりも書き込みの方がスケールさせやすい作りになっていて、クラスタの台数を増やせばこれぐらいの書き込みペースは普通に捌くことができます。弊社ではそれなりにメモリとストレージを積んだ20台ぐらいで済んでます。当然、複数台のクラスタを管理するコストが増えるし、新しいミドルウェアの知識やパフォーマンスチューニングも必要だし、複数DBと実質的に同じ問題を抱えることになるので、開発業務に与える負荷は非常に高い選択肢です。しかし、求められる書き込み量の桁が2桁とか3桁とか上がってしまうと、RDBに書くという手段ではどうにもならなくなるのでやらざるを得ないという感じですね。この分野で他に使えそうなミドルウェア/サービスは、ScyllaDB、DynamoDB、BigTable辺りでしょうか。メモリキャッシュに近い方向ならHazelcastとかIgniteとかAerospikeみたいなのもあります。

Railsで秒間1000コミットを捌くにはどうすればいいのか (Kaigi on Railsのフリースペースより) - joker1007’s diary