A new release of the MongoDB Go Driver has been made available by the Go Driver Team. Version 1.0.4 has a number of bug fixes and improvements which are listed in the release notes for 1.0.4. The changes include:
- When reading a whole file at once, the next read now returns
- When using a cursor with a legacy server and limit set, we no longer leak a connection.
- Query failures are now properly wrapped in a
- Running ListCollections against a legacy server now filters indexes.
- Specified behavior where connection string contains authentication database but no credentials.
General documentation for the MongoDB Go Driver is available on GoDoc and on the MongoDB Documentation site. BSON library documentation is also available on GoDoc. Questions can be asked through the mongo-go-driver Google Group and bug reports should be filed against the Go project in the MongoDB JIRA. Your feedback on the Go driver is greatly appreciated.
MongoDB.local Is Coming To London
On September 25th, MongoDB.local London will open its doors to the UK and European users of MongoDB. With a keynote from CTO and Co-founder Eliot Horowitz bring everyone up to date with the latest developments in the MongoDB universe and a look at where it's heading, followed by four tracks of talks, the day is going to be humongously informative.
Revolutionizing Data Storage and Analytics with MongoDB Atlas on Google Cloud and HCL
Every organization requires data they can trust—and access—regardless of its format, size, or location. The rapid pace of change in technology and the shift towards cloud computing is revolutionizing how companies handle, govern and manage their data by freeing them from the heavy operational burden of on-premise deployments. Enterprises are looking for a centralized, cost-effective solution that allows them to scale their storage and analytics so they can ingest data and perform artificial intelligence (AI) and machine learning (ML) operations, ultimately expanding their marketing horizon. This blog post explores why companies should partner with MongoDB Atlas on Google Cloud to begin their data revolution journey, and how HCL Technologies can support customers looking to migrate. MongoDB Atlas as the distributed data platform MongoDB Atlas is the leading database-as-a-service on the market for three main reasons: Unparalleled developer experience - allows organizations to bring new features to market at a high velocity Horizontal scalability - supports hundreds of terabytes of data with sub-second queries Flexibility - stores data to meet various regulatory, operational, and high availability requirements. The versatility offered by MongoDB’s document model makes it ideal for modern data-driven use cases that require support for structured, semi-structured, and unstructured content all within a single platform. Its flexible schema allows changes to support new application features without costly schema migrations typically required with relational databases. MongoDB Atlas extends the core database by offering services like Atlas Search and MongoDB Realm that are a necessity for modern applications. Atlas Search provides a powerful Apache Lucene-based full text search engine that automatically indexes data in your MongoDB database without the need for a separate dedicated search engine or error-prone replication processes. Realm provides edge-to-cloud sync and backend services to accelerate and simplify mobile and web development. Atlas’ distributed architecture supports horizontal scaling for data volume, query latency, and query throughput which offers the scalability benefits of distributed data storage alongside the rich functionality of a fully-featured general purpose database. MongoDB Atlas is unique in its ability to provide the most wanted database as a managed service and is relied on by the world’s largest companies for their mission-critical production applications. Innovation powered by collaboration with HCL Technologies MongoDB’s versatility as a general-purpose database, in addition to its massive scalability, makes it a perfect foundation for analytics, visualization, and AI/ML applications on Google Cloud. As an MSP partner for Google Cloud, HCL Technologies helps enterprises accelerate and risk-mitigate their digital agenda, powered by Google Cloud. We’ve successfully implemented applications leveraging MongoDB Atlas on Google Cloud, building upon MongoDB’s flexible JSON-like data model, rich querying and indexing, and elastic scalability in conjunction with Google Cloud’s class-leading cloud infrastructure, data analytics, and machine learning capabilities. HCL is working with some of the world’s largest enterprises in building secure, performant, and cost-effective solutions with MongoDB and Google. Possessing technical expertise in Google Cloud, MongoDB, machine learning, and data science, our dedicated team developed a reference architecture that ensures high performance and scalability. This is simplified by MongoDB Atlas’ support for Google Cloud services which allows it to essentially operate as a cloud-native solution. Highlighted features include: Integration with Google Cloud Key Management Service Use of Google Cloud’s native storage snapshot for fast backup and restore Ability to create read-only MongoDB nodes in Google Cloud to reduce latency with Google Cloud-native services regardless of where the primary node is located (even other public cloud providers!) Integrated billing with Google Cloud Ability to span a single MongoDB cluster across Google Cloud regions worldwide, and more As represented in Figure 1 below, MongoDB Atlas on Google Cloud can be used as a single database solution for transactional, operational, and analytical workloads across a variety of use cases. Figure 1: MongoDB's core characteristics and features The following architecture in Figure 2 demonstrates the ease of reading and writing data to MongoDB from Google Cloud services. Dataflow, Cloud Data Fusion, and Dataproc can be leveraged to build data pipelines to migrate data from heterogeneous databases to MongoDB and to feed data to create interactive dashboards using Looker. These data pipelines support both batch and real-time ingestion workloads and can be automated and orchestrated using Google Cloud - native services.. Figure 2: MongoDB Atlas' integration with core Google Cloud services A data platform built using MongoDB Atlas and Google Cloud offers an integrated suite of services for storage, analysis, and visualization. Address your business challenges with HCL: Industry use cases Data-driven solutions built with MongoDB Atlas on Google Cloud have multiple applications across industries such as financial services, media and entertainment, healthcare, oil and gas, energy, manufacturing, retail, and the public sector. Every industry can benefit from this highly integrated storage and analytical solution. Use Cases and Benefits Data lake modernization with low cost and high availability for media and entertainment customers: Maintaining high availability and a low-cost data lake is an obstacle for any online entertainment platform that builds mobile or web ticketing applications. However, building on Google App Engine with MongoDB Atlas Clusters in the backend allows for a high-availability, low-cost data platform that seamlessly feeds data to downstream analytics platforms in real time. Unified data platform for retail customers: The retail business frequently requests an agile environment in order to encourage innovation among its engineers. With its agility in scaling and resource management, seamless multi-region clusters, and premium monitoring, running MongoDB Atlas on Google Cloud is a fantastic choice for building a single data platform. This simplifies the management of different data platforms and allows developers to focus on new ideas. High-speed real-time data platform of supply chain system for manufacturing units: By having real-time visibility and distributed data services, supply chain data can become a competitive advantage. MongoDB Atlas on Google Cloud provides a solid foundation for creating distributed data services with a unified, easy-to-maintain architecture. The unrivaled speed of MongoDB Atlas simplifies supply chain operations with real-time data analytics. The way forward Even in just the past decade, organizations have been forced to adapt to the extremely fast pace of innovation in the data analytics landscape: moving from batch to real-time, on-premise to cloud, gigabytes to petabytes, and the increased accessibility of advanced AI/ML models thanks to providers like Google Cloud. With our track record of success in this domain, HCL Technologies is uniquely positioned to help organizations realize the joint benefits of building data analytics applications with best-of-breed solutions from Google Cloud and MongoDB. Visit us to learn more about the HCL Google Ecosystem Business Unit and how we can help you harness the power of MongoDB Atlas and Google Cloud Platform to change the way you store and analyze your data through these solutions.