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Cassandra Development & Consulting Services
Leverage our expertise and boost your Cassandra project



Our Expertise






Services
APACHE CASSANDRA CONSULTING
Apache Cassandra consultants at Altoros offer support and maintenance services focused on every aspect of your database, including the investigation of the existing system, design, deployment, and evaluation.


MIGRATION TO A NEW DATABASE
Our team of experienced Apache Cassandra developers can help you migrate to a new database with custom ETL (extract, transform, load) solutions and integration with legacy applications. Leverage our operational experience to run migration with no downtime.
APACHE CASSANDRA DEPLOYMENT AND CONFIGURATION
Deploy a scalable Apache Cassandra database to the cloud. Our experts have your back 24/7, monitoring the performance of your cluster and ensuring you can focus on the most important aspects of your business.


APPLICATION DEVELOPMENT USING APACHE CASSANDRA AS A DATASTORE
We will help you to design, develop, and deploy applications with Apache Cassandra database, which handles large data with no failure probability.
APACHE CASSANDRA PERFORMANCE OPTIMIZATION
At Altoros, we offer support and maintenance services that are focused on every aspect of your database. We help our customers to investigate, design, deploy, and evaluate their solutions.


DATA LAKE AND DATA WAREHOUSE DESIGN
Altoros helps you to drive business intelligence by quickly aggregating, integrating, structuring, and storing data from disparate sources. With current and historical data gathered in a single point, you get a consolidated view of your business processes.
APACHE CASSANDRA BENCHMARKING BASED ON YOUR USE CASES
To fine-tune your Apache Cassandra and make it easily meet the requirements, our consultants will inspect your solution based on your use cases, remove bottlenecks, and deliver greater performance management.

Development
Let's discuss your project
Get your consultation nowData engineering
Altoros has first-hand experience in NoSQL databases: our experts assisted the core teams in the development of data stores, integration with other technologies, and fine-tuning performance.

Our engineers developed the NuoDB Migrator, which enables users of traditional SQL databases to easily migrate their data to NuoDB.

The Altoros team contributed to the core of the product.

Altoros benchmarked a number of Redis-based products: Redis Cloud, ElastiCache, openredis, RedisGreen, and Redis To Go.
Big data expertise at Altoros has been acknowledged by Clutch (ex-SourcingLine), a research company based in Washington, DC, for two years in a row:




WHY PARTNER WITH US
Find out more about the technology
Contact Altoros’s expertsRelated cases
The team at Altoros has successfully implemented 1400+ projects, some of which can be accessed through this page. We do also share more details on a particular project and other stories of success on demand. Please do not hesitate to reach out to us with a request!


Optimizing a Cloud-Native Analytical Platform for Healthcare
The customer
The company is a California-based provider of innovative solutions for healthcare—distributed globally. The customer focuses on creating products for elderly managed care, chronic disease care, hospital discharge care, consumer health, and telemedicine.
The need
The customer was developing a cloud-based platform that would aggregate and analyze patient data gathered by its health scanner. The delivered solution aimed at simplifying the process of provisional diagnostics and therapy assignment using a mobile phone.
After some time, the company faced performance bottlenecks. The platform did not process the captured patient data within 5–10 seconds; the developers were not sure about how scalable the platform is.
The customer was under a tight deadline, so it turned to Altoros to find bottlenecks, ensure scalability, and accelerate time to market.
The challenge
In the course of the project, the following challenges were identified:
- The system should be scalable enough to process huge amounts of data—collected from 200,000+ sensors.
- All of the sensitive data should be properly secured.
- Since the recommendations provided by the platform may affect health of patients, it should operate faultlessly and be highly available.
- The product should be flexible for future updates—i.e. easily maintained
The outcome
Partnering with Altoros, the customer delivered a cloud-native microservicesbased platform that analyzes thousands of health data packets within 5–10 seconds. By employing Cloud Foundry, the customer reduced time to market by accelerating the development process: pushing new releases now take minutes instead of hours.
In Q3 2016, the platform was launched to analyze data gathered from 200,000 of smart sensors. In the next 1.5 years, it is expected that the number of platform users will grow to 1,500,000–2,000,000 patients and medical stuff.
Technology stack
Server platform - Cloud Foundry
Infrastructures - OpenStack, AWS
Programming languages - Java, JavaScript, R
Technologies - Node.js, Apache Spark, Artillery, Kafka
Database - Apache Cassandra (DataStax Enterprise)




A New-Generation Energy Management System
The customer
The customer is a global company providing affordable antenna solutions, eco-friendly energy saving software, and medical devices of the highest quality design. The company regularly performs researches to ensure conformity to the best standards before any device is manufactured.
Galtronics wanted to support the Green IT technologies by enabling users to access power consumption data without paper bills. The company turned to Altoros to build an energy management system that would be able to record, store, and manage petabytes of power consumption data and scale up from a single server to thousands of machines.
The need
The energy management system had to record and store petabytes of data. It had to start with collecting the electricity consumption data from devices and appliances located in 1,000 houses, which would result in 50,000 records written to the database every 15 seconds.
Every record had to be not only stored, but also processed to enable power consumption management by room, house, or device.
The challenge
The company planned to add 4,000 houses to the system shortly, which would result in 5,000,000 records sent to the database every 15 seconds. The customer’s plans to provide symHome to electricity companies across the US, so the system has to feature the ability to scale next to endlessly. Therefore it was needed to enable adding big amount of data that should be processed without delays, as well as, ensuring data storage and computing during a long period of the system operation.
The outcome
Monitoring energy usage enables saving from 5 to 15 percent on electricity bills. The system can be scaled next to endlessly and the number of houses that are connected to it grows.
Partnering with Altoros the customer managed to:
- save the efforts on keeping the system sustainability, as the architecture provides the possibility to scale in future being very cost-effective
- concentrate on creating new features or optimizing the existing ones reduce the equipment cost by 32% (due to utilizing free software, which doesn’t require special server hardware)
- reduce the equipment cost by 32% (due to utilizing free software, which doesn’t require special server hardware)
The application helps the company to achieve its Green IT objectives by continuously reducing the environmental impact and encouraging eco-friendly life-style.
Technology stack
Client Platform/Application Server - Apache Tomcat 6.0
Technologies - Spring Framework 3.0, Quartz, JSP 2.1, Adobe Flex SDK 3.5, Apple iOS SDK 4.3, Hadoop
Programming Languages - Java, ActionScript 3.0, Objective-C
Database - MySQL, Cassandra
Development Environment - Eclipse, Flash Builder 4, Xcode 3.2.6




The SNP Detection System
The customer
The customer helps scientists and laboratories to conduct research and experiments in the field of life sciences. Their key services include next-generation sequencing, bioanalytical and mass spectrometry, as well as DNA sequencing. The customer turned to Altoros to develop a solution that would detect SNP in digitized DNA sequences saved in the FASTA/FASTQ format easier and less time-consuming.
The need
A common problem for researchers who work on genome analysis is the need to store and process terabytes of data fast. To address this issue, Altoros delivered an automated system for single-nucleotide polymorphism detection that provides better performance at a smaller cost. Deployed on Amazon public cloud, it was powered by Amazon Web Services and Amazon EMR. With this optimal solution our customer was able to process 150 GB of genome sequencing data within 24 hours and in the most cost-efficient manner.
The challenges
Apart from building an algorithm for detecting SNP, we were to determine what hardware configuration could provide the required data processing speed.
The outcome
Altoros delivered an automated system for singlenucleotide polymorphism detection that provides better performance at a smaller cost. Deployed on Amazon public cloud, it was powered by Amazon Web Services and Amazon EMR. With this optimal solution, our customer was able to process 150 GB of genome sequencing data within 24 hours and in the most cost-efficient manner.
We started with the development of a prototype to test the possible deployment options and make sure the functionality works correctly. The system for SNP detection was later installed on the customer’s private distributed infrastructure and data processing was performed with Apache Hadoop.
Technology stack
Server Platform - Linux, Amazon Web Services
Client Platform/Application Server - Internet Explorer, Firefox, Safari, Chrome
Technologies - Map / Reduce, Java, HTML, Apache Hadoop, Amazon EMR
Programming Language - Perl, Java, Bash
Database/Storage - HDFS
Development Environmen - Linux editors, Java IDE, Amazon AWS console




Scalable, Preventive, Real-Time Monitoring of Railway Crossings
The customer
The company is a global provider of technologies, infrastructure, as well as vehicles for rail transportation. Its portfolio includes railway signaling, control, electrification, and automation systems. The customer also produces commuter, regional, high-speed, and intercity trains / locomotives.
The need
The company had a legacy system for monitoring railway crossings—tracking accidents and equipment malfunctions. However, the solution was designed as a monolithic app, which failed to scale and made it difficult to introduce new modules and functionality. So, the customer partnered with Altoros to achieve flexibility in maintenance and to sustain petabytes of data from multiple devices installed at the railway stations. Having achieved this, critical notifications about potential or happening accidents needed to be delivered in real-time.
The challenges
Under the project, the team at Altoros had to address the following issues:
- The app deployment required a lot of time-consuming manual steps, which slowed down the delivery of critical functionality into production.
- The IoT system needed to support a range of old devices installed at the stations—until they got replaced with the new ones.
The outcome
Collaborating with Altoros, the customer delivered a scalable solution for monitoring railway crossings and notifying about accidents or malfunctions in real time. With a microservices architecture, the company can now easily extend functionality without affecting the whole system. The solution can also sustain petabytes of data daily, processing megabytes per second. The system already gathers IoT data from nearly 5,000 edge devices installed at 2,500 railway crossings in the USA and Canada.
Technology stack
Platform - Kubernetes
Programming languages - Java, Python
Technologies - Node.js, Cloudera, Apache Kafka, HiveMQ, MQTT, Docker, TensorFlow
Databases - Couchbase Server, MongoDB, PostgeSQL, HDFS




Reducing Infrastructure Costs and Release Cycles for a FinTech Vendor
The customer
The company is a U.S. provider of currency hedging and exchange execution services to investors and financial institutions. Since its foundation in 2013, the customer raised $4.1 million in funding.
The need
The company had a platform that facilitated currency hedging and asset management to mitigate risks imposed by currency fluctuations. Nine global corporations were using the platform to offset potential losses on investments due to exchange rates. At some point, the customer recognized value in white-labeling its product. As part of this plan, the organization partnered with one of the leading financial holding companies in the world, growing its clientele to 95 corporations.
Each of these corporations had different processes, systems, and technology stacks that the platform needed to easily integrate and comply with. This required introducing a lot of customizations in an agile manner.
Relying on Altoros, the customer wanted to automate and speed up feature delivery. With the white-labeling strategy in force, the company also sought scalability, high availability, and ease of maintenance.
The challenges
Under the project, the team at Altoros had to address the following issues:
- As the platform analyzed sensitive financial information, ensuring proper data security was crucial.
- It was important to maintain high integrability of the product due to different internal processes, technology stacks, etc., of corporations using the system.
-
1 week
from idea to production
-
2x–4x
faster delivery
-
2x
less infrastructure costs
The outcome
Partnering with Altoros, the customer was able to successfully execute its white-labeling strategy by offering a FinTech product that eases an adoption curve, features high integrability, and minimizes customization. The migration to a cloud-native PaaS that has a mature ecosystem of services enabled the company to reduce release time by 2x–4x, cut down expenses on infrastructure maintenance by 2x, as well as ensure scalability and high availability. Thanks to a variety of the implemented security measures, the organization can also address the primary concerns of the financial industry.
Technology stack
Platform Heroku
Programming language Ruby
Frameworks and tools Ruby on Rails, React.js, Amazon S3, Amazon SNS, Amazon QuickSight, Amazon SageMaker, Redis, Code Climate, RuboCop
Databases PostgreSQL, MongoDB




Web-Based System for Retailers
The customer
TapMap is a start-up company that needed a solution to synchronize the retailer’s Point of Sale system with an online account. The stock and prices are updated daily in the Web system. Shoppers can access this data with the help of a free app that works on iPhone or Android. It enables them to compare prices by scanning barcodes with the built-in camera of a smartphone.
The need
As customers search and scan products, the Web system collects, processes, and generates reports for retailers. Searchesand scans are displayed on a visual map in real time.Browsing reports, retailers can track the shopping trends minute-by-minute for a single product or the entire stock. They canuse this information further for the products analytics. A retailer can, for example, compare the demand for Chardonnay in anumber of districts during a month and forecast the amount of wine needed to be in stock in the next month. One can evencalculate the expected sales and open a new POS in the area where the demand has proved to be sufficient.
The challenges
To collect, process, and display the statistics in real time, the system must feature the highest level of performance and scalability. The existing solution was slow and could not satisfy the growing needs of the customer.
The outcome
The customer has been named one of three winners at the International EXPO 2011. The event was held in the heart of Silicon Valley at the Plug and Play Tech Centre and hosted the world’s leading technology experts. The iEXPO featured promising startups who presented their project pitches to the audience.
Technology stack
Server Platform - CentOS
Client Platform/Application Server - Web browser/Nginx + Passenger, Apache + Passenger
Technologies - Ruby on Rails 2.3.8, Sphinx, MongoDB, MapReduce
Programming Language - Ruby 1.8.7
Database - MySQL 5.1 (primary), MongoDB 1.8 (for statistics)
Development Environment - TextMate




LikeFolio: Best practices of Cloud and Ruby development for Application Optimization
The customer
Likefolio.com is an application based on the concept “invest in what you like,” which helps users to find potential investment opportunities through analyzing brand awareness in social networks. It aggregates conversations, status updates, likes, and check-ins from social networks into a proprietary database. Then it links these keywords and phrases to publicly traded companies and translates the data into investment ideas.
The need
SwanPowers, LLC turned to Altoros to apply best practices in cloud transformation Ruby development to their existing system in order to satisfy the high requirements to the level of concurrency, load, response times, etc.
The challenge
The Web site was based on a distributed architecture that was not able to scale as expected. In addition, there were several issues with DB contention and background jobs.
It was estimated that the solution would have to serve 10,000 users simultaneously with at least 100 new user registrations per minute.
Every new user would need to view a partial portfolio right after signing up. The system had to interact with APIs of multiple social networks and remain within the limits of request quotas. It also had to apply complex business logic to extract investment information from social network data.
Additionally, LikeFolio profiles of every existing user had to be synchronized with all the latest updates on their social networks.
The outcome
Thanks to the improvements introduced by our team, the customer was able to launch several marketing campaigns without being afraid of performance issues due to increased traffic. The application’s overall uptime and performance have been improved significantly.
Taking into account the LikeFolio’s focus on social networks, Altoros made sure that it complies with the required API quota/limits.
Technology stack
Server Platform - Amazon Web services (EC2, Route 53, CloudWatch, RDS), Redis
Client Platform/Application Server - Unicorn
Programming Language - Ruby
Technologies - Ruby on Rails, Sidekiq, Capistrano
Database - Amazon RDS for MySQL





The Cloud-based Document Exchange System
The customer
Normally, e-mail service providers limit the size of data sets that can be sent at a time. In addition, users have to employ third-party software to create documents with complex structures, multiple fields, diagrams, and tables. The customer is a provider of advanced document exchange services who designed software that eliminates these drawbacks. The new system simplifies the process of creating documents for various types of businesses and industries. Users can exchange documents, preview correspondence, create custom templates for documents and letters, assign various access restrictions, review, and mark up documents.
The system was deployed in a cloud environment to provide a scalable data storage and ensure high availability of all services.
The need
The customer is a start-up company that came to Altoros with an idea to develop a service similar to “virtual FedEx” that delivers electronic documents. The main advantage of the emerging system was to be a simple and intuitive user interface. Ease of navigation and availability of the planned features were tested on a prototype developed by Altoros.
The challenge
The system had to store a large number of documents and provide fast access to any of them. It was decided to use cloud technologies to ensure high availability, quick response times, and easy horizontal scalability. The system was integrated with EMC, FileTransfer services, and a third-party Reporting Tool selected by Altoros’s experts. The system had to be integrated with local document management solutions of the customers.
The outcome
The customer released a new solution that makes document exchange easier and more efficient. The system has already become rather popular, thanks to a user-friendly interface, smart navigation, enhanced security, and unlimited storage space in the cloud. By testing prototypes, we were able to carefully study all requirements, confirm functionality, and access usability before we started development.
Technology stack
Server Platform - CentOS
Client Platform/Application Server - CentOS
Technologies - Couchbase, Solr, Pentaho, Spring MVC, jQuery
Programming Language - Java
Database - Cassandra
Database Design Tool - yEd
Development Environment - Eclipse


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About Altoros
Altoros is a professional software development company with headquarters in Pleasanton (USA), branch offices in Norway and Finland, and development centers in the USA, Canada, Argentina, Ireland, Poland, Moldova, Turkey, and Georgia. With 20-year IT experience and a strong team of full-stack software engineers and consultants, we help our clients to achieve unsurpassed quality at all stages of the web, mobile, and desktop application development. By providing multiple time-zone teams and different formats of working (onsite, remote, hybrid, etc.), we help organizations across the globe to gain sustainable competitive advantage through the adoption of innovative technologies.Research & Development
The R&D department within Altoros keeps track of the latest technologies available on the market from 2011. The main goal of the department is to learn how projects can be developed faster, better, more effectively, and more efficiently. Our studies are mostly focused on big data solutions, data science, cloud computing, and cross-platform development.Contact us now
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Headquarters
4900 Hopyard Rd., Suite 100Pleasanton, CA 94588 -
Altoros Finland OY
Kyllikinportti 2,00240 Helsinki, Finland -
Altoros Norge AS
Tordenskiolds gate 2, 0160 Oslo, Norway -
Development Center, Poland
Młynarska st. 42 /115,01-171 Warsaw -
Development center, Argentina
Buenos AiresAv. Federico Lacroze 2827,C1426CPP CABA, ArgentinaSanta Fe25 de Mayo 2884, S3000FUASanta Fe, Argentina