Google Cloud Bigtable Integrations

Google Cloud is an online service that lets you create everything from simple websites to complex apps for businesses of any size. Customers who are new to the system will receive $300 in credits for testing, deploying, and running workloads. Customers can use up to 25+ products free of charge. Use Google's core data analytics and machine learning. All enterprises can use it. It is secure and fully featured. Use big data to build better products and find answers faster. You can grow from prototypes to production and even to planet-scale without worrying about reliability, capacity or performance. Virtual machines with proven performance/price advantages, to a fully-managed app development platform. High performance, scalable, resilient object storage and databases. Google's private fibre network offers the latest software-defined networking solutions. Fully managed data warehousing and data exploration, Hadoop/Spark and messaging.

BigQuery is a serverless, multicloud data warehouse that makes working with all types of data effortless, allowing you to focus on extracting valuable business insights quickly. As a central component of Google’s data cloud, it streamlines data integration, enables cost-effective and secure scaling of analytics, and offers built-in business intelligence for sharing detailed data insights. With a simple SQL interface, it also supports training and deploying machine learning models, helping to foster data-driven decision-making across your organization. Its robust performance ensures that businesses can handle increasing data volumes with minimal effort, scaling to meet the needs of growing enterprises. Gemini within BigQuery brings AI-powered tools that enhance collaboration and productivity, such as code recommendations, visual data preparation, and intelligent suggestions aimed at improving efficiency and lowering costs. The platform offers an all-in-one environment with SQL, a notebook, and a natural language-based canvas interface, catering to data professionals of all skill levels. This cohesive workspace simplifies the entire analytics journey, enabling teams to work faster and more efficiently.

It takes only days to wrap any data source with a single reference Data API and simplify access to reporting and analytics data across your teams. Make it easy for application developers and data engineers to access the data from any source in a streamlined manner. - The single schema-less Data API endpoint - Review, configure metrics and dimensions in one place via UI - Data model visualization to make faster decisions - Data Export management scheduling API Our proxy perfectly fits into your current API management ecosystem (versioning, data access, discovery) no matter if you are using Mulesoft, Apigee, Tyk, or your homegrown solution. Leverage the capabilities of Data API and enrich your products with self-service analytics for dashboards, data Exports, or custom report composer for ad-hoc metric querying. Ready-to-use Report Builder and JavaScript components for popular charting libraries (Highcharts, BizCharts, Chart.js, etc.) makes it easy to embed data-rich functionality into your products. Your product or service users will love that because everybody likes to make data-driven decisions! And you will not have to make custom report queries anymore!

InfluxDB is a purpose-built data platform designed to handle all time series data, from users, sensors, applications and infrastructure — seamlessly collecting, storing, visualizing, and turning insight into action. With a library of more than 250 open source Telegraf plugins, importing and monitoring data from any system is easy. InfluxDB empowers developers to build transformative IoT, monitoring and analytics services and applications. InfluxDB’s flexible architecture fits any implementation — whether in the cloud, at the edge or on-premises — and its versatility, accessibility and supporting tools (client libraries, APIs, etc.) make it easy for developers at any level to quickly build applications and services with time series data. Optimized for developer efficiency and productivity, the InfluxDB platform gives builders time to focus on the features and functionalities that give their internal projects value and their applications a competitive edge. To get started, InfluxData offers free training through InfluxDB University.

Enhance your market research efforts by utilizing Auris, an AI-driven platform for consumer insights. Leveraging advanced deep learning models, Auris processes streaming data to deliver critical insights that are significantly more extensive in sample size, providing real-time information at a fraction of the traditional costs. The platform harnesses alternative data sources, including consumer conversations, feedback, and reviews, which require refinement to eliminate noise and enrich various attributes. Auris facilitates this cleaning process, enabling you to gain valuable insights quickly and in the format that suits your needs. Trust Auris to strengthen your marketing strategy and execution by capturing the essence of what matters to you—your brand, your product, and your interests. This encompasses a wide array of sources such as social media, review platforms, news feeds, influential blogs, and even insights about your competitors! With the power of Artificial Intelligence and Neuro-Linguistic Programming at your disposal, you can effectively transform this raw data into structured information that drives decision-making and enhances your competitive edge. Embrace this innovative approach to market research and watch your strategies flourish.

Cloud IoT Core is a comprehensive managed service designed to facilitate the secure connection, management, and data ingestion from a vast array of devices spread across the globe. By integrating with other services on the Cloud IoT platform, it offers a holistic approach to the collection, processing, analysis, and visualization of IoT data in real-time, ultimately enhancing operational efficiency. Leveraging Cloud Pub/Sub, Cloud IoT Core can unify data from various devices into a cohesive global system that works seamlessly with Google Cloud's data analytics services. This capability allows users to harness their IoT data streams for sophisticated analytics, visualizations, and machine learning applications, thereby improving operational workflows, preempting issues, and developing robust models that refine business processes. Additionally, it enables secure connections for any number of devices—whether just a few or millions—through protocol endpoints that utilize automatic load balancing and horizontal scaling, ensuring efficient data ingestion regardless of the situation. As a result, businesses can gain invaluable insights and drive more informed decision-making processes through the power of their IoT data.

Dataproc enhances the speed, simplicity, and security of open source data and analytics processing in the cloud. You can swiftly create tailored OSS clusters on custom machines to meet specific needs. Whether your project requires additional memory for Presto or GPUs for machine learning in Apache Spark, Dataproc facilitates the rapid deployment of specialized clusters in just 90 seconds. The platform offers straightforward and cost-effective cluster management options. Features such as autoscaling, automatic deletion of idle clusters, and per-second billing contribute to minimizing the overall ownership costs of OSS, allowing you to allocate your time and resources more effectively. Built-in security measures, including default encryption, guarantee that all data remains protected. With the JobsAPI and Component Gateway, you can easily manage permissions for Cloud IAM clusters without the need to configure networking or gateway nodes, ensuring a streamlined experience. Moreover, the platform's user-friendly interface simplifies the management process, making it accessible for users at all experience levels.

Now, anyone can quickly uncover insights buried within their company data in just seconds. With search functionality, users can analyze their data and receive automated insights precisely when needed. ThoughtSpot enables anyone to pose questions, discover insights, and delve deeply into their organizational data almost instantaneously. No longer do you have to wait for specialized reports from data professionals; you can now address ad-hoc data inquiries immediately. This empowers individuals without technical backgrounds to find answers to their own data-related questions while you create a comprehensive source of truth that maintains security and governance across the board. By maximizing the potential of your cloud data warehouse, you can enhance the speed at which insights are obtained for everyone in your organization. In just minutes, democratize access to insights and revolutionize how your company leverages data. Explore how leading-edge companies are utilizing ThoughtSpot to extract greater value from their data resources. Furthermore, it can be deployed as either SaaS or as software within your private virtual cloud, ensuring that AI-driven insights are available to you more promptly than ever before. Ultimately, this transformative approach to data usage can lead to significant improvements in business decision-making.

VPC Service Controls provide a managed networking capability for your resources within Google Cloud. New users are offered $300 in complimentary credits to use on Google Cloud within their first 90 days of service. Additionally, all users can access certain products like BigQuery and Compute Engine at no cost, within specified monthly limits. By isolating multi-tenant services, you can significantly reduce the risks associated with data exfiltration. It is crucial to ensure that sensitive information is accessible solely from authorized networks. You can further restrict access to resources based on permitted IP addresses, specific identities, and trusted client devices. VPC Service Controls also allow you to define which Google Cloud services can be accessed from a given VPC network. By enforcing a security perimeter through these controls, you can effectively isolate resources involved in multi-tenant Google Cloud services, thereby minimizing the likelihood of data breaches or unauthorized data access. Furthermore, you can set up private communication between cloud resources, facilitating hybrid deployments that connect cloud and on-premises environments seamlessly. Leverage fully managed solutions such as Cloud Storage, Bigtable, and BigQuery to enhance your cloud experience and streamline operations. These tools can significantly improve efficiency and productivity in managing your cloud resources.

All-in-one platform to connect your APIs and services in the most efficient way. Allow busy developers to create complex integrations that no-code tools can't solve. JavaScript is a powerful tool that allows you to create more code in a shorter time. Audit code changes, audit credentials creation, use, and check execution logs. Open data streams, transactions and caches, errors management, logging support, multiple environments support, and reused functions can all be used. Execute tasks on demand, using a schedule approach or a webhook. Integration in your systems infrastructure is as easy as possible in a matter of minutes. JS modules allow you to reuse your business logic. They include a friendly editor, powerful integrations and libraries, as well as a friendly source code editor. Kubernetes can be deployed on-premise. External identity providers and enhanced auditing and logging. You can write your scripts from a web browser and then run them in the YepCode cloud.

The Apache Hadoop software library serves as a framework for the distributed processing of extensive data sets across computer clusters, utilizing straightforward programming models. It is built to scale from individual servers to thousands of machines, each providing local computation and storage capabilities. Instead of depending on hardware for high availability, the library is engineered to identify and manage failures within the application layer, ensuring that a highly available service can run on a cluster of machines that may be susceptible to disruptions. Numerous companies and organizations leverage Hadoop for both research initiatives and production environments. Users are invited to join the Hadoop PoweredBy wiki page to showcase their usage. The latest version, Apache Hadoop 3.3.4, introduces several notable improvements compared to the earlier major release, hadoop-3.2, enhancing its overall performance and functionality. This continuous evolution of Hadoop reflects the growing need for efficient data processing solutions in today's data-driven landscape.

Apache Spark™ serves as a comprehensive analytics platform designed for large-scale data processing. It delivers exceptional performance for both batch and streaming data by employing an advanced Directed Acyclic Graph (DAG) scheduler, a sophisticated query optimizer, and a robust execution engine. With over 80 high-level operators available, Spark simplifies the development of parallel applications. Additionally, it supports interactive use through various shells including Scala, Python, R, and SQL. Spark supports a rich ecosystem of libraries such as SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming, allowing for seamless integration within a single application. It is compatible with various environments, including Hadoop, Apache Mesos, Kubernetes, and standalone setups, as well as cloud deployments. Furthermore, Spark can connect to a multitude of data sources, enabling access to data stored in systems like HDFS, Alluxio, Apache Cassandra, Apache HBase, and Apache Hive, among many others. This versatility makes Spark an invaluable tool for organizations looking to harness the power of large-scale data analytics.

JanusGraph stands out as a highly scalable graph database designed for efficiently storing and querying extensive graphs that can comprise hundreds of billions of vertices and edges, all managed across a cluster of multiple machines. This project, which operates under The Linux Foundation, boasts contributions from notable organizations such as Expero, Google, GRAKN.AI, Hortonworks, IBM, and Amazon. It offers both elastic and linear scalability to accommodate an expanding data set and user community. Key features include robust data distribution and replication methods to enhance performance and ensure fault tolerance. Additionally, JanusGraph supports multi-datacenter high availability and provides hot backups for data security. All these capabilities are available without any associated costs, eliminating the necessity for purchasing commercial licenses, as it is entirely open source and governed by the Apache 2 license. Furthermore, JanusGraph functions as a transactional database capable of handling thousands of simultaneous users performing complex graph traversals in real time. It ensures support for both ACID properties and eventual consistency, catering to various operational needs. Beyond online transactional processing (OLTP), JanusGraph also facilitates global graph analytics (OLAP) through its integration with Apache Spark, making it a versatile tool for data analysis and visualization. This combination of features makes JanusGraph a powerful choice for organizations looking to leverage graph data effectively.

TIMi allows companies to use their corporate data to generate new ideas and make crucial business decisions more quickly and easily than ever before. The heart of TIMi’s Integrated Platform. TIMi's ultimate real time AUTO-ML engine. 3D VR segmentation, visualization. Unlimited self service business Intelligence. TIMi is a faster solution than any other to perform the 2 most critical analytical tasks: data cleaning, feature engineering, creation KPIs, and predictive modeling. TIMi is an ethical solution. There is no lock-in, just excellence. We guarantee you work in complete serenity, without unexpected costs. TIMi's unique software infrastructure allows for maximum flexibility during the exploration phase, and high reliability during the production phase. TIMi allows your analysts to test even the most crazy ideas.

Multi-cloud data security with a single pane of glass Industry's first SaaS access governance solution. Cloud is fragmented and data is scattered across different systems. Sensitive data is difficult to access and control due to limited visibility. Complex data onboarding hinders data scientist productivity. Data governance across services can be manual and fragmented. It can be time-consuming to securely move data to the cloud. Maximize visibility and assess the risk of sensitive data distributed across multiple cloud service providers. One system that enables you to manage multiple cloud services' data policies in a single place. Support RTBF, GDPR and other compliance requests across multiple cloud service providers. Securely move data to the cloud and enable Apache Ranger compliance policies. It is easier and quicker to transform sensitive data across multiple cloud databases and analytical platforms using one integrated system.

IntelliPay is a comprehensive payment processing and recurring billing solution designed for businesses operating on SaaS, subscription, and service models. Our platform supports both traditional fee structures and no-cost models where cardholders cover the processing fees. We seamlessly integrate with top payment gateways and provide a variety of payment options, including Online Payments, Virtual, On-Demand, Recurring Installments, EMV, and Custom Portals. Historically, businesses that accepted payments would typically bear the costs associated with processing fees, but our innovative technology and recent regulatory changes now empower you with choices. You can either continue to absorb these fees or transfer them to your customers, allowing you to retain a larger portion of your revenue. We offer diverse options that can reduce or eliminate costs for organizations across various industries. To explore our solutions tailored to specific sectors, please refer to the list provided below. Our versatile enterprise payment platform is designed to be highly scalable, easily integrable, and to enhance revenue growth significantly. Additionally, we ensure that patients have access to every available payment method without adding extra strain on your staff.

OpenTSDB comprises a Time Series Daemon (TSD) along with a suite of command line tools. Users primarily engage with OpenTSDB by operating one or more independent TSDs, as there is no centralized master or shared state, allowing for the scalability to run multiple TSDs as necessary to meet varying loads. Each TSD utilizes HBase, an open-source database, or the hosted Google Bigtable service for the storage and retrieval of time-series data. The schema designed for the data is highly efficient, enabling rapid aggregations of similar time series while minimizing storage requirements. Users interact with the TSD without needing direct access to the underlying storage system. Communication with the TSD can be accomplished through a straightforward telnet-style protocol, an HTTP API, or a user-friendly built-in graphical interface. To begin utilizing OpenTSDB, the initial task is to send time series data to the TSDs, and there are various tools available to facilitate the import of data from different sources into OpenTSDB. Overall, OpenTSDB's design emphasizes flexibility and efficiency for time series data management.

Heroic is an open-source monitoring solution initially developed at Spotify to tackle challenges related to the large-scale collection and near real-time analysis of metrics. It comprises a limited number of specialized components that each serve distinct purposes. The system offers indefinite data retention, contingent upon adequate hardware investment, alongside federation capabilities that enable multiple Heroic clusters to connect and present a unified interface. A key component, Consumers, is tasked with the consumption of metrics, illustrating the system's design for efficiency. During the development of Heroic, it became evident that managing hundreds of millions of time series without sufficient context poses significant challenges. Additionally, the federation support facilitates the handling of requests across various independent Heroic clusters, allowing them to serve clients via a single global interface. This feature not only streamlines operations but also minimizes geographical traffic, as it allows individual clusters to function independently within their designated zones. Such capabilities ensure that Heroic remains a robust choice for organizations needing effective monitoring solutions.

Infometry's Google Connectors facilitate the seamless integration of Google Applications with Informatica Cloud IDMC, previously recognized as IICS. Their Google Sheets Connectors are fully certified by Informatica and offer native interfaces for users. By utilizing Infometry's Connectors, organizations can achieve smooth integration and access real-time data analytics. The Google Connector for Informatica simplifies application integration, data extraction for downstream systems, and ETL processes for Enterprise Data Warehouses. Customers who utilize Informatica Cloud Connectors often store various datasets in Google Sheets, including Sales Forecasts, Goals, Product Master records, SKU data, Lab Results, Headcount estimates, and OpEx Budgets, which all need to be efficiently transferred to Enterprise Data Warehouses, Cloud Applications, and Data Lakes. Infometry developed a Google Sheet connector that leverages Informatica’s native interface, encompassing comprehensive API operations such as reading, writing, updating, deleting, and searching among other functionalities, ensuring a robust solution for data management needs. This integration empowers businesses to leverage their existing data in Google Sheets for enhanced analytics and decision-making capabilities.

Data processing that integrates both streaming and batch operations while being serverless, efficient, and budget-friendly. It offers a fully managed service for data processing, ensuring seamless automation in the provisioning and administration of resources. With horizontal autoscaling capabilities, worker resources can be adjusted dynamically to enhance overall resource efficiency. The innovation is driven by the open-source community, particularly through the Apache Beam SDK. This platform guarantees reliable and consistent processing with exactly-once semantics. Dataflow accelerates the development of streaming data pipelines, significantly reducing data latency in the process. By adopting a serverless model, teams can devote their efforts to programming rather than the complexities of managing server clusters, effectively eliminating the operational burdens typically associated with data engineering tasks. Additionally, Dataflow’s automated resource management not only minimizes latency but also optimizes utilization, ensuring that teams can operate with maximum efficiency. Furthermore, this approach promotes a collaborative environment where developers can focus on building robust applications without the distraction of underlying infrastructure concerns.

Excel and Google Sheets are often delicate and susceptible to mistakes, leading to collaboration that frequently requires tedious and repetitive tasks. Files that hold significant information can break unexpectedly, frequently without anyone noticing, resulting in decisions based on inaccurate data. Highly compensated employees end up wasting countless hours on monotonous tasks, while workflows necessitate constant manual oversight, draining essential cognitive resources. You can selectively share portions of your file, whether that be specific cell ranges or entire sheets. With Layer, you can offload all these repetitive tasks efficiently. Every change is clearly marked for you, eliminating the need to search for updates. You will have access to a comprehensive record of modifications, spreadsheet iterations, and all communications. We are transforming the way teams collaborate on spreadsheets by adding a productivity layer to both Excel and Google Sheets. In truth, we have grown weary of the conventional perks that businesses provide today, as they often fall short of addressing the real challenges faced in collaborative work environments. By focusing on enhancing efficiency and reducing errors, we aim to redefine how teams interact with data.