Questions we got at AWS re:Invent

Peter Zawistowicz
December 9, 2016 | Updated: April 4, 2017
#Cloud #Company

Last week, the MongoDB team had the pleasure of attending the world’s foremost cloud computing conference. At AWS re:Invent in Las Vegas, we heard our CTO explain how Thermo Fisher reduced mass spectrometry experiment times from days to minutes, we announced the general availability of MongoDB 3.4, and we formally introduced MongoDB Atlas to the AWS community.

Here are some of the most popular questions we were asked last week:

“I’m new to cloud technology. Tell me…”

Where does my application server live and where does my database live?
Depending on your architecture, your app server may run on a different machine or in a different data center. For most applications, latency can be significantly reduced by spinning up your app server and database in regions closest to your user traffic. For example, MongoDB Atlas can currently run in any of 4 supported Amazon regions, with more being added soon!

What’s the easiest way to migrate to the cloud?
As Amazon dramatically revealed in their keynote this year, cloud migration is a very hot topic. Short of uploading all of your data to an Amazon 18-wheeler, there are many options for migration. Depending on your availability requirements, this can be as simple as creating a copy of your data in the cloud and re-routing traffic to the cloud-hosted database. For more mission critical deployments, migration can be achieved with no downtime. We also provide tailored consulting services to help you create a migration path.

“I’m new to MongoDB. Can you explain…”

What are secondary indexes and how do I use them?
Indexes are powerful tools that let you significantly improve query performance by avoiding full collection scans. Secondary indexes can be added to highly-queried fields (or sets of fields) to extend these performance gains to additional queries, aggregations, or sorts. Adding too many indexes can unnecessarily harm read/write performance, so we recommending using MongoDB Compass to identify which indexes are being used and where additional indexes would be most beneficial.

What are the benefits of MongoDB’s native aggregation functionality?
MongoDB’s aggregation framework gives you the capability to run advanced analytics processing right inside the database, rather than in your application. Since MongoDB 2.2, the aggregation framework has been significantly expanded to include many of the rich aggregation features you’d expect from a relational database.

Does MongoDB offer support?
Yes. Creating a MongoDB Atlas account means you have access to assistance with the UI and support for any connectivity issues. You also have our global site reliability engineering team focused on monitoring and ensuring availability of the service.

For support of the underlying database software, we recommend a MongoDB Enterprise Advanced or MongoDB Professional subscription. We also offer a subscription designed specifically for MongoDB Atlas customers. These subscriptions include access to our support engineers 24 hours a day, 365 days a year. Our support team not only helps you troubleshoot specific questions, but can also guide you through upgrades and suggest optimizations. Support plans are also available for applications in development. Learn more about MongoDB support.

“MongoDB Atlas looks great! Tell me more about…”

How secure is MongoDB Atlas? Is my data encrypted?
MongoDB Atlas is secure by default. Each MongoDB Atlas group is provisioned into its own AWS Virtual Private Cloud, isolating your data and underlying systems from other MongoDB Atlas users. You can even directly peer your MongoDB Atlas VPC with your application servers deployed to AWS using private IP addresses.

We also configure network encryption and access control by default, and IP whitelists (enforced with AWS Security Groups) let you limit access to a specific range of IP addresses. MongoDB engineers automatically apply security patches as soon as they are released.

Finally, you can elect to encrypt your storage volumes at no additional cost using using AWS's encrypted EBS volumes.

How does MongoDB Atlas handle backups?
Backups can be configured optionally and are billed by the size of the data being backed up. Backups in MongoDB Atlas are continuous and maintained seconds behind the production cluster, meaning you can conduct point-in-time restores.

MongoDB Atlas backups are stored in MongoDB’s private data centers for added redundancy.

“I’m thinking about using DynamoDB…”

How does MongoDB Atlas compare?
There are a few features in MongoDB Atlas that our users find valuable:

Rich query language: The MongoDB Query language gives you the querying capabilities you’d expect from a SQL-based database with the flexibility and performance of NoSQL.
Native aggregation: MongoDB’s aggregation framework gives you the capability to run advanced analytics processing right inside the database, rather than in your application.
Secondary indexing support: Support for secondary indexes allows you to extend the performance gains of traditional indexing to additional fields or field sets.
Robust tooling: MongoDB was released in 2009 and, in addition to the vast ecosystem of tooling that lets you support, monitor, and visualize your deployment with confidence, MongoDB Atlas is built using the expertise gained from years of building enterprise-grade products for the most complex, mission critical deployments.
Choice of infrastructure vendor (coming soon!): With upcoming support for Google Cloud Platform and Microsoft Azure, you’ll be able to choose where your deployment lives based on your app requirements.

“I’m currently using a 3rd party hosted MongoDB service...”

How does MongoDB Atlas compare?
MongoDB Atlas is built and supported by the team who builds the database. This means you get access to the latest security patches, major releases, and features when they’re released—not a year later!

MongoDB Atlas is also the most cost-effective way to run MongoDB 3.2 or 3.4 with our WiredTiger storage engine. With hourly billing and independently-tuneable memory, storage size, and disk I/O, you only pay for what you need.

All MongoDB Atlas clusters are hosted in isolated, secure VPCs which mean you aren’t sharing bandwidth with other databases within the same virtual machine.

Download MongoDB Atlas Best Practice Guide

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Thermo Fisher & MongoDB: Moving Apps to the Cloud with MongoDB & AWS

Biotechnology giant uses MongoDB Atlas and an assortment of AWS technologies and services to reduce experiment times from days to minutes . Thermo Fisher (NYSE: TMO) is moving its applications to the public cloud as part of a larger Thermo Fisher Cloud initiative with the help of offerings such as MongoDB Atlas and Amazon Web Services. Last week, our CTO & Cofounder Eliot Horowitz presented at AWS re:Invent with Thermo Fisher Senior Software Architect Joseph Fluckiger on some of the transformative benefits they’re seeing internally and across customers. This recap will cover Joseph’s portion of the presentation. Joseph started by telling the audience that Thermo Fisher is maybe the largest company they’d never heard of. Thermo Fisher employs over 51,000 people across 50 countries, with over $17 billion in revenues in 2015. Formed a decade ago through the merger of Thermo Electron & Fisher Scientific, it is one of the leading companies in the world in the genetic testing and precision laboratory equipment markets. The Thermo Fisher Cloud is a new offering built on Amazon Web Services consisting of 35 applications supported by over 150 Thermo Fisher developers. It allows customers to streamline their experimentation, processing, and collaboration workflows, fundamentally changing how researchers and scientists work. It serves 10,000 unique customers and stores over 1.3 million experiments, making it one of the largest cloud platforms for the scientific community. For internal teams, Thermo Fisher Cloud has also streamlined development workflows, allowing developers to share more code and create a consistent user experience by taking advantage of a microservices architecture built on AWS. One of the precision laboratory instruments the company produces is a mass spectrometer, which works by taking a sample, bombarding it with electrons, and separating the ions by accelerating the sample and subjecting it to an electric or magnetic field. Atoms within the sample are then sorted by mass and charge and matched to known values to help customers figure out the exact composition of the sample in question. Joseph’s team develops the software powering these machines. Thermo Fisher mass spectrometers are used to: Detect pesticides & pollutants — anything that’s bad for you Identify organic molecules on extraplanetary missions Process samples from athletes to look for performance-enhancing substances Drive product authenticity tests And more *Mass Spectrometry Results* *MS Instrument Connect* During the presentation, Joseph showed off one application in the Thermo Fisher Cloud called MS Instrument Connect, which allows customers to see the status of their spectrometry instruments with live experiment results from any mobile device or browser. No longer does a scientist have to sit at the instrument to monitor an ongoing experiment. MS Instrument Connect also allows Thermo Fisher customers to easily query across instruments and get utilization statistics. Supporting MS Instrument Connect and marshalling data back and forth is a MongoDB cluster deployed in MongoDB Atlas, our hosted database as a service. Joseph shared that MongoDB is being used across multiple projects in Thermo Fisher and the Thermo Fisher Cloud, including Instrument Connect, which was originally deployed on DynamoDB. Other notable applications include the Thermo Fisher Online Store (which was migrated from Oracle), Ion Reporter (which was migrated from PostgreSQL), and BioPharma Finder (which is being migrated from SQL Lite). *Thermo Fisher apps moving to MongoDB* To support scientific experiments, Thermo Fisher needed a database that could easily handle a wide variety of fast-changing data and allow its customers to slice and dice their data in many different ways. Experiment data is also very large; each experiment produces millions of “rows” of data. When explaining why MongoDB was chosen for such a wide variety of use cases across the organization, Joseph called the database a “swiss army knife” and cited the following characteristics: High performance High flexibility Ability to improve developer productivity Ability to be deployed in any environment, cloud or on premises What really got the audience’s attention was a segment where Joseph compared incumbent databases that Thermo Fisher had been using with MongoDB. MongoDB compared to MySQL (Aurora) If I were to reduce my slides down to one, this would be that slide,” Joseph stated, “This is absolutely phenomenal. What we did was we inserted data into MongoDB & Aurora and with only 1 line of code, we were able to beat the performance of MySQL. *"If I were to reduce my slides down to one, this would be that slide.”* In additional to delivering 6x higher performance with 40x less code, MongoDB also helped reduce the schema complexity of the app. MongoDB compared to SQL Lite For the mass spectrometry application used in performance enhancing drug testing, Thermo Fisher rewrote the data layer from SQL Lite to MongoDB and reduced their code by a factor of about 3.5. MongoDB compared to DynamoDB Joseph then compared MongoDB to DynamoDB, stating that while both databases are great and easy to deploy , MongoDB offers a more powerful query language for richer queries to be run and allows for much simpler schema evolution. He also reminded the audience that MongoDB can be run in any environment while DynamoDB can only be run on AWS. Finally, Joseph showed an architecture diagram showing how MongoDB is being used with several AWS technologies and services (including AWS Lambda, Docker, & Apache Spark) to parallelize algorithms and significantly reduce experiment processing times. *Reducing experiment times from days to minutes* He concluded his presentation by explaining why Thermo Fisher is pushing applications to MongoDB Atlas, citing its ease of use, the seamless migration process, and how there has been no downtime, even when reconfiguring the cluster. The company began testing MongoDB Atlas around its release date in early July and began launching production applications on the service in September. With the time Thermo Fisher team is saving by using MongoDB Atlas (that would have otherwise been spent on writing and optimizing their data layer), they’re able to invest more time in improving their algorithms, their customer experience, and their processing infrastructure. Anytime I can use a service like MongoDB Atlas, I’m going to take that so that we at Thermo Fisher can focus on what we’re good at, which is being the leader in serving science. To view Joseph & Eliot’s AWS re:Invent presentation in its entirety, click here . -- Get started with MongoDB Atlas today!

December 8, 2016

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Using MongoDB Skill Scanner to Build Better Training Programs

Technology leaders know that transformation is about more than just adopting modern technologies like MongoDB. The entire organization has to rally behind change — which is no easy task. The skills that modern development teams need are evolving faster than ever, and hiring to fill skills gaps can be too time-consuming and expensive of a process for many organizations. So it’s imperative that we plan for how we want to bring our people with us on our modernization journey, and proactively upskill them on the technologies we’re betting on. Because what happens if you choose MongoDB, but your developers don’t know how to use it? CIOs know that training programs are easier said than done. EY reported that 30% of CIOs acknowledge that their training programs are ineffective, and that they’re struggling to retain talent because of it. These leaders come to us to help them build and execute their MongoDB training programs , and seek advice on two extremely common yet critical challenges: How do we get away from the less effective one-size-fits-all approach? How do we measure the ROI of our training program and connect it to business impact? How we use MongoDB Skill Scanner to overcome training challenges Our Professional Services team uses a tool called MongoDB Skill Scanner to address both of these challenges. This tool helps us provide these three benefits to our customers looking to build a training program: Improve MongoDB proficiency: Teams can use Skill Scanner to quickly and easily assess the MongoDB skill gaps of their team members and gain a comprehensive understanding of their team’s MongoDB skills baseline. Increased productivity and accuracy: When team members have a comprehensive understanding of MongoDB, they are able to work more quickly and accurately on projects, leading to increased productivity and a higher quality of work. Save time and money with targeted Training: Using Skill Scanner, customers can avoid wasting time and money on trial-and-error learning. Instead, they can focus on improving their skills in a more targeted and efficient way with right-sized training plans. By leveraging this data, our customers’ engineers can engage in the right training at the right time, targeted for their job role and specific skill shortages. When a training program is built this way, engineers maximize their knowledge retention and minimize time away from their projects. Skill Scanner includes three role-based assessments, one for developers, database administrators, and DevOps respectively. Through a series of multiple choice questions, Skill Scanner provides customers with a clear understanding of their level of expertise across a set of technical skills that are critical for success in their role. After submitting the assessment, engineers will get results in each skill area outlining if they are beginner, intermediate, or advanced. Why data-driven training programs matter We’ve learned that it’s not enough to just tell teams to go watch training videos or webinars on their own, or to place everyone in the same one-size-fits-all program. Skills gaps vary from team to team, and individual to individual. The one-size-fits-all approach of some programs may not address individual learners' needs, wasting time and making it difficult for them to acquire new skills. By using Skill Scanner, we’re able to interpret this data to help determine which training courses your team should take. But we don’t only capture this data before doing training; we use Skill Scanner again after training programs are completed to see where immediate improvements have been made. This helps technology leaders prove the impact and ROI of their training, and gives them the confidence that their teams are ready to be successful with MongoDB. Developing a Precision Learning Program To go even further, our team can work with you to build a Precision Learning Program, where we use Skill Scanner data to build learning schedules that are unique to each individual. These schedules include a variety of short, blended, learning events such as classes, technical workshops, self-paced exercises, and project coaching. We’ve seen PLP lead to higher knowledge retention and of course, measurable project results. A customer who recently concluded their PLP saw a 43% increase in knowledge retention. Getting started building a personalized training program Skill gaps aren’t a novel problem IT leaders are facing. But with new digital courses, training, and technologies, the resources to close these gaps are at your fingertips. Skill Scanner and Precision Learning Program have been specifically designed to empower teams by offering targeted training that enhances their understanding of MongoDB. These short training events are carefully crafted to close skill gaps without compromising developer productivity. We’ve seen a variety of customers use this tool to help train their team’s individual needs, from needing to upskill new hires on their teams, projects with new MongoDB products, migrating to MongoDB Atlas, and more. It also saves your business the hours developers would've wasted searching for answers (and developers don’t want to spend their time that way, either). “We need help getting from point A to point B and feel MongoDB is uniquely positioned to help” — CTO at large insurance firm If you're interested in trying out MongoDB Skill Scanner or want to explore the MongoDB Precision Learning Program further, you can reach out to your account representative or contact us directly .

June 7, 2023