Our 5 years of practice in big data as well as our hands-on experience with Apache Cassandra create the perfect background for Qrapp to render Cassandra consulting services and cover the needs of your business. To make sure that you get the most out of the technology, our consultants can perform a 360-degree range of Cassandra-related services helping you:
To get a better understanding of what exactly our Cassandra consulting capabilities are, check the list of our cooperation models.
Our consultants will bring in deep knowledge of Apache Cassandra to help you define your big data strategy. With our help, you’ll be fully capable of:
Our team can help you get a better and deeper understanding of the database as an integral component of your solution’s architecture. To do that, we will share our expertise and advise you on the ways to answer a variety of your architecture-related questions, for instance:
To safeguard Cassandra’s high working speeds, our consultants will turn to best practices in Cassandra’s data modeling. They will design your model from scratch, keeping the number of data reads at minimum and spreading data evenly across the cluster. Alternatively, our specialists can make adjustments and improvements to the data model of an already existing solution.
To fine-tune your Cassandra and make it easily meet your requirements and expectations, our consultants will inspect your solution to find bottlenecks and remove them. Or, if it’s not possible for some technical reason, our specialists will increase their throughput capacity to maximum. For the purpose of a successful Cassandra performance tuning, we can:
You may encounter overdue or even non-completed tasks because of Cassandra’s performance fluctuations, occasional errors and the like. Or you may be unhappy with your high computing power costs and network overload. Regardless of these problems’ nature, our Cassandra consultants will target problematic areas and find solutions to the underlying issues.
To give you a better idea of the use cases where Cassandra fits perfectly, here we mention some of its most common applications.
Cassandra is a good fit for storing sensor data, which makes it cover a lot of industry-related ground: healthcare, manufacturing, logistics, real estate and so on. Our specialists will design Cassandra’s data model to enable efficient key-based data lookups. And, besides that, they will ensure that Cassandra’s writes are incredibly cheap and lightning-quick to make Cassandra work well with huge amounts of incoming data. This is exactly what sensor data requires.
Messaging systems (instant messaging, collaboration apps an so on) require new messages to be written easily and quickly to the database. And that’s what Cassandra does perfectly. Besides, such solutions usually don’t require lots of updates, which is good because it is one of Cassandra’s weak spots. Also, Cassandra’s compaction may suit such systems’ needs: our specialists can tune the solution to periodically delete old irrelevant data. And if you want to set up a popular feature for messages to only be available within a limited time, we can give every message a “time to live” and it will be erased automatically. This will reduce costs of storing and deleting data.
Cassandra’s write efficiency, read speed and data model’s design make the database suited for user activity tracking on ecommerce and entertainment websites. We can set up all needed Cassandra features to store data on the products a visitor looked through, the movies they watched or the games they played. And on top of that, we can integrate Cassandra with an analytical tool of your choice to define visitors’ preferences on-the-fly and recommend them products/movies/books/articles they may like.
With Cassandra, banks can not only get a 360-degree customer view but also expand the list of their security features with, say, fraud detection. Qrapp can safeguard Cassandra’s high availability, which will enable banks to be sure that their security features are always up and running. We can properly tune Cassandra’s read performance to make data about a particular user easily extractable for analysis. And besides that, our consultants can set up Cassandra’s seamless integration with Apache Spark to analyze potential fraud in real time.
Problem. Apache Cassandra is targeted at write operations. But what about reads? Having both writes and reads perform quickly and neatly may seem unachievable, which is why you’d have to choose only one of the alternatives.
Solution. To achieve fast and always available read performance, Cassandra replicates data and writes it onto multiple nodes, creates and stores various versions of one table tailored to suit different queries. It may seem like a burden for the write that will adversely affect it. But all these additional efforts don’t usually cause any write-related problems. Initially, Cassandra’s write is so efficient that sacrificing a bit of its performance to improve reads will hardly affect it. However, to make sure your solution’s write and read performance are in balance, our consultants can design a data model suitable for your solution and tune your data duplication and replication policies.
Problem. Cassandra provides good possibilities for scaling up linearly. But in some cases, it may not be enough just to add nodes to your cluster to reach it.
Solution. So that you could enjoy Cassandra’s linear scalability, our specialists can inspect your database, find out what keeps the performance down and remove the limitations. Particular measures would be specific to every business, but, as an example, we could create a new data model for your database. An optimized data model will significantly contribute to your solution’s performance, since it will reflect the way you query your data, which speeds up data lookups.
Problem. Although Cassandra’s read is quick, intensive read workloads that your machine learning (ML) creates can cause serious performance issues for Cassandra.
Solution. To secure high performance of all the elements of your solution without machine learning workloads getting in the way, we can review your existing data model and primary key composition policy. After the review, we will spot alarming weaknesses, perform optimizations and create new versions of your tables tasked with supporting ML queries. Besides that, if needed, we may set up an additional cluster within one of your data centers that will be targeted exclusively at supporting ML.
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