We hope you enjoyed part 1 of our Back to Basics series where we introduced you to NoSQL and MongoDB.
In part II we will actually get to code and build a blogging application. We will start in the Mongo Shell to show you how to interact directly with MongoDB on the command line, then move to an IDE to show you how to build a complete web application with the MongoDB Python driver.
Once your application is built we will show you how to add indexes to improve the performance of your queries. Like most databases, indexes can dramatically improve query performance. MongoDB comes with a query analyser and a tool called Explain that can give detailed insight into query performance.
At the end of this webinar you will know how to:
- Run MongoDB
- Create a basic database and set of collections using the MongoDB Shell
- Create a basic database and collection using one of our language drivers
- Add an index to a collection to improve query performance
- Review the efficiency of your queries using the explain framework
Register now for this webinar and all the remaining webinars in the series.
MongoDB Connector for Apache Spark: Announcing Early Access Program & New Spark Course
**Update: August 4th 2016** Since this original post, the connector has been declared generally available, for production usage. Click through for a tutorial on using the new MongoDB Connector for Apache Spark . We live in a world of “big data”. But it isn’t only the data itself that is valuable – it’s the insight it can generate. How quickly an organization can unlock and act on that insight has become a major source of competitive advantage. Collecting data in operational systems and then relying on nightly batch ETL (Extract Transform Load) processes to update the Enterprise Data Warehouse (EDW) is no longer sufficient. Speed-to-insight is critical, and so analytics against live operational data to drive real-time action is fast becoming a necessity, enabled by a new generation of technologies like MongoDB and Apache Spark. The new native MongoDB Connector for Apache Spark provides higher performance, greater ease of use, and access to more advanced Spark functionality than any connector available today. The new MongoDB University course for Apache Spark provides a fast track introduction for developers and data scientists building new generations of operational applications incorporating sophisticated real-time analytics. The Rise of Apache Spark Apache Spark is one of the fastest-growing big data projects in the history of the Apache Software Foundation. With its memory-oriented architecture, flexible processing libraries, and ease-of-use, Spark has emerged as a leading distributed computing framework for real-time analytics. As a general-purpose framework, Spark is used for many types of data processing – it comes packaged with support for machine learning, interactive queries (SQL), statistical queries with R, graph processing, ETL, and streaming. Spark allows programmers to develop complex, multi-step data pipelines using a directed acyclic graph (DAG) pattern. It supports in-memory data sharing across DAGs, so that different jobs can work with the same data. Additionally, Spark supports a variety of popular programming languages including Scala, Java, and Python. Sign up for the new Spark course at MongoDB University. For loading and storing data, Spark integrates with a number of storage and messaging platforms including Amazon S3, Kafka, HDFS, machine logs, relational databases, NoSQL datastores, MongoDB, and more. MongoDB and Spark Today While MongoDB natively offers rich real-time analytics capabilities , there are use cases where integrating the Spark engine can extend the processing of operational data managed by MongoDB. This allows users to operationalize results generated from Spark within real-time business processes supported by MongoDB. Examples of users already using MongoDB and Spark to build modern-data driven applications include: A multinational banking group operating in 31 countries with 51 million clients has implemented a unified real-time monitoring application with Apache Spark and MongoDB . The platform enables the bank to improve customer experience by continuously monitoring client activity across its online channels to check service response times and identify potential issues. A global manufacturing company estimates warranty returns by analyzing material samples from production lines. The collected data enables them to build predictive failure models using Spark machine learning and MongoDB. A video sharing website is using Spark with MongoDB to place relevant advertisements in front of users as they browse, view, and share videos. A global airline has consolidated customer data scattered across more than 100 systems into a single view stored in MongoDB. Spark processes are run against the live operational data in MongoDB to update customer classifications and personalize offers in real time, as the customer is live on the web or speaking with the call center. Artificial intelligence personal assistant company x.ai uses MongoDB and Spark for distributed machine learning problems. There are a number of ways users integrate MongoDB with Spark. For example, the MongoDB Connector for Hadoop provides a plug-in for Spark. There are also multiple 3rd party connectors available. Today we are announcing the early access to a new native Spark connector for MongoDB. Introducing the MongoDB Connector for Apache Spark The new MongoDB Connector for Apache Spark provides higher performance, greater ease of use and, access to more advanced Spark functionality than the MongoDB Connector for Hadoop. The following table compares the capabilities of both connectors. MongoDB Connector for Spark MongoDB Connector for Hadoop with Spark Plug-In Written in Scala, Spark’s native language Yes No, Java Support for Scala, Java, Python & R APIs Yes Yes Support for the Spark interactive shell Yes Yes Support for native Spark RDDs Yes No Java RDDs. More verbose and complex to work with Support for Spark DataFrames and Datasets Yes DataFrames Only Schema must be manually inferred Automated MongoDB schema inference Yes No Support for Spark core Yes Yes Support for Spark SQL Yes Yes Support for Spark Streaming Yes Yes Support for Spark Machine Learning Yes Yes Support for Spark GraphX Yes No Data locality awareness Yes The Spark connector is aware which MongoDB partitions are storing data No Support for MongoDB secondary indexes to filter input data Yes Yes Support for MongoDB aggregation pipeline to filter input data Yes No Compatibility with MongoDB replica sets and sharded clusters Yes Yes Support for MongoDB 2.6 and higher Yes Yes Support for Spark 1.6 and above Yes Yes Supported for production usage Not Currently Available for early access evaluation Yes Written in Spark’s native language, the new connector provides a more natural development experience for Spark users as they are quickly able to apply their Scala expertise. The connector provides access to the Spark interactive shell for data exploration and rapid prototyping. The connector exposes all of Spark’s libraries, enabling MongoDB data to be materialized as DataFrames and Datasets for analysis with SQL (benefiting from automatic schema inference), streaming, machine learning, and graph APIs. The Spark connector can take advantage of MongoDB’s aggregation pipeline and rich secondary indexes to extract, filter, and process only the range of data it needs – for example, analyzing all customers located in a specific geography. This is very different from more simple NoSQL datastores that do not offer either secondary indexes or in-database aggregations. In these cases, Spark would need to extract all data based on a simple primary key, even if only a subset of that data is required for the Spark process. This means more processing overhead, more hardware, and longer time-to-insight for the analyst. To maximize performance across large, distributed data sets, the Spark connector is aware of data locality in a MongoDB cluster. RDDs are automatically co-located with the associated MongoDB shard to minimize data movement across the cluster. The nearest read preference can be used to route Spark queries to the closest physical node in a MongoDB replica set, thus reducing latency. Review the MongoDB Connector for Spark documentation to learn how to get started with the connector, and view code snippets for different APIs and libraries. Fast Track to Apache Spark: New MongoDB University Course To get the most out of any technology, you need more than documentation and code. Over 350,000 students have registered for developer and operations courses from MongoDB University. Now developers and budding data scientists can get a quick-start introduction to Apache Spark and the MongoDB connector with early access to our new online course. Getting Started with Spark and MongoDB provides an introduction to Spark and teaches students how to use the new connector to build data analytics applications. In this course, we provide an overview of the Spark Scala and Java APIs with plenty of sample code and demonstrations. Upon completing this course, students will be able to: Outline the roles of major components in the Spark framework Connect Spark to MongoDB Source data from MongoDB for processing in Spark Write data from Spark into MongoDB The course does not assume a prior knowledge of Spark, but does require an intermediate level of expertise with MongoDB. The course is free. Sign up at MongoDB University . Next Steps To wrap up, we are very excited about the possibilities Spark and MongoDB present together, and we hope with the new connector and course, you will be well on your way to building modern, data-driven applications. We would love to hear from you as you explore this new connector and put it through its paces - you can provide feedback and file bugs under the MongoDB Spark Jira project . Here’s a summary of how to get started: Read the MongoDB Connector for Spark documentation and download the connector If you have any questions, please send them to the MongoDB user mailing list Sign up for the new Spark course at MongoDB University
Australian Start-Up Ynomia Is Building an IoT Platform to Transform the Construction Industry and its Hostile Environments
The trillion dollar construction industry has not yet experienced the same revolution in technology you might have expected. Low levels of R&D and difficult working environments have led to a lack of innovation and fundamental improvements have been slow. But one Australian start-up is changing that by building an Internet of Things (IoT) platform to harness construction and jobsite data in real time. “Productivity in construction is down there with hunting and fishing as one of the least productive industries per capita in the entire world. It's a space that's ripe for people to come in and really help,” explains Rob Postill , CTO at Ynomia. Ynomia has already been closely involved with many prestigious construction projects, including the residential N06 development in London’s famous 2012 Olympic Village. It was also integral to the construction of the Victoria University Tower in Australia. Link to Podcast Episode Here “These projects involve massive outflow of money: think about glass facades on modern buildings, which can represent 20-30 percent of the overall project cost. They are largely produced in China and can take 12 weeks to get here,” says Postill. “Meanwhile, the plasterer, the plumber, the electrician are all waiting for those glass facades to be put on so it is safe for them to work. If you get it wrong, you can go in the deep red very quickly.” To tackle these longstanding challenges, Ynomia aims to address the lack of connectivity, transparency and data management on construction sites, which has traditionally resulted in the inefficient use of critical personnel, equipment and materials; compressed timelines; and unpredictable cash flows. To optimize productivity, Ynomia offers a simple end-to-end technology solution that creates a Connected Jobsite. Helping teams manage materials, tools, and people across the worksite in real time. IOT in a Hostile Environment The deployment of technology in construction is often fraught with risk. As a result, construction sites are still largely run on paper, such as blueprints, diagrams and models as well as the more traditional invoices and filing. At the same time, there is a constant need to track progress and monitor massive volumes of information across the entire supply chain. Engineers, builders, electricians, plumbers, and all the other associated professionals need to know what they need to do, where they need to be, and when they need to start. “The environment is hostile to technology like GPS, computers, and mobile phone reception because you have a lot of Faraday cages and lots of water and dust,” explains Postill. “You can't have somebody wandering around a construction site with a laptop; it'll get trashed pretty quickly." Enter MongoDB Atlas “On a site, you might be talking about materials, then if you add to that plant & equipment, or bins, or tools etc, you're rapidly getting into thousands and thousands of tags, talking all the time, every day,” said Postill. That means thousands of tags now send millions of readings on Ynomia building sites around the world. All these IoT data packets must be stored efficiently and accurately so Ynomia can reassemble the history of what has happened and track tagged inventory, personnel, and vehicles around the site. Many of the tag events are also safety critical so accuracy is a vital component and packets can't be missed. To address these needs Ynomia was looking for a database that was scalable, flexible, resilient and could easily handle a wide variety of fast-changing sensor data captured from multiple devices. The final component Postill was looking for in a database layer was freedom: a database that didn't lock them into a single cloud platform as they were still in the early stages of assessing cloud partners. The Commonwealth Scientific and Industrial Research Organisation , which Postill had worked with in the past, suggested MongoDB , a general purpose, document-based database built for modern applications. “The most important factor was that the database is event-driven, which I knew would be difficult in the traditional relational model. We deal with millions of tag readings a day, which is a massive wall of data,” said Postill. A Cloud Database Ynomia is using MongoDB Atlas , the global cloud database service, now hosted on Microsoft Azure. Atlas offers best-in-class automation and proven practices that combine availability, scalability, and compliance with the most demanding data security and privacy standards. “When we started we didn't know enough about the problem and we didn't want to be constrained," explained Postill. "MongoDB Atlas gives us a cloud environment that moves with us. It allows us to understand what is happening and make changes to the architecture as we go." Postill says this combination of flexibility and management tooling also allows his developers to focus on business value not undifferentiated code. One example Postill gave was cluster administration: "Cluster administration for a start-up like us is wasted work," he said. "We’re not solving the customer's problem. We're not moving anything on. We’re focusing on the wrong thing. For us to be able to just make that problem go away is huge. Why wouldn’t you?" Atlas also gives Ynomia the option to spin out new clusters seamlessly anywhere in the world. This allows customers to keep data local to their construction site, improving latency and helping solve for regional data regulations. Real Time Analytics The company has also deployed MongoDB Charts, which takes this live data and automatically provides a real time view. Charts is the fastest and easiest way to visualize event data directly from MongoDB in order to act instantly and decisively based on the real-time insights generated by event-driven architecture. It allows Ynomia to share dashboards so all the right people can see what they need to and can collaborate accordingly. “Charts enables us to quickly visualize information without having to build more expensive tools, both internally and externally, to examine our data,” comments Postill. “As a startup, we go through this journey of: what are we doing and how are we doing it? There's a lot of stuff we are finding out along the way on how we slice and re-slice our data using Charts.” A Platform for Future Growth Ynomia is targeting a huge market and is set for ambitious growth in the coming years. How the platform, and its underlying architecture, can continue to scale and evolve will be crucial to enabling that business growth. “We do anything we can to keep things simple,” concluded Postill. “We pick technology partners that save us from spending time we shouldn't spend so we can solve real problems. We pick technologies that roll with the punches and that's MongoDB.” When we started we didn't know enough about the problem and we didn't want to be constrained," explained Postill. "MongoDB Atlas gives us a cloud environment that moves with us. It allows us to understand what is happening and make changes to the architecture as we go. Rob Postill, CTO, Ynomia