Endless Possibilities
Innovative Data Processing with Kubernetes
One of the platforms that was early embraced is Kubernetes. As a scalable infrastructure layer, it was a good fit for Squidflow approach. The fact that Kubernetes uses scaling architecture, provides much lower cost that the scale-up architecture. Another contributing factor is that implementation of software fault-tolerance is far less expensive than implementing it in hardware fault-tolerance servers.
Streamlined Big Data Development with Hadoop and Hive
01. Simplified Distributed Computing
02.Efficient Data Processing
03. Focus on Data Logic
Our data solution development approach always considers the best technology for a client or project. For that reason, we carefully choose technology so that it matches needs and goals of the project. In a wide range of NoSQL platforms, we use Cassandra as a distributed, scalable, and fault-tolerant database designed to for storing large datasets. It is particularly useful when ever we expect large volumes of data inserts, because that’s what Cassandra is optimized for.
Some of the most successful implementations under Squidflow belt used Spark for processing of big data. Spark offers cost-effective data processing at scale using affordable hardware or low cost virtual machines. It is an in-memory cluster computing framework and it provides a simple programming interface which our developers extensively utilize to make use of the CPU, memory, and storage resources across a cluster of servers.
Squidflow recognize some key benefits of Spark:
- Easy to use
- Fast
- Scalable
- General use
- Fault tolerant
Spark’s ease of use comes from a rich application programming interface (API). It provides more than 80 data processing operators, which makes it more expressive than other similar platforms. Spark makes its cluster computing capabilities available to an application in the form of a library. Libraries can be written in all Spark API available languages, such as Scala, Java, Python, and R.
Another feature that makes Spark attractive to our development practice is its immense speed. It is orders of magnitude faster than Hadoop MapReduce, which measures in hundreds of times in some cases.
We appreciate Spark’s scalability, because processing capacity of a cluster can be increased by simply adding more nodes to a cluster. That enables us to begin with a small cluster, and as dataset grows over the time, we can add more computing capacity. Scaling that way is not only smart, but also economical.
Our solutions utilize Spark not only for batch processing, but also for machine learning, graph computing, stream processing, and interactive analysis. That’s how we can use one framework for a variety of purposes, thus avoiding multi-platform challenges and increasing efficiency of our team.
In a cluster of a few hundred nodes, the probability of a node failing on any given day is high. Spark is fault tolerant, and automatically handles the failure of a node in a cluster.