KNIME for Supply Chain Management
Reading Time: 7 minutes KNIME can be used to develop customised components of Supply Chain Management, from Inventory plan to forecast solutions.
Let’s Clean that Code
Reading Time: 8 minutes Writing a clean code that nobody mocks is a little difficult, but by cultivating a habit, following best practices, it is not impossible to achieve.
Apache Spark’s Join Algorithms
Reading Time: 4 minutes Joins in Apache Spark are fundamental transformations, but if you are not familiar with their internal algorithm, they can become too expensive.
Knime Meets Apache Kafka
Reading Time: 4 minutes Knime taps into the power of Apache Kafka’s pub-sub mechanism by introducing Kafka extension. It adds nodes to connect, read and publish to Kafka cluster.
Shared Variables in Distributed Computing
Reading Time: 4 minutes Spark provides two shared variables in distributed computing which are accessible to all the nodes in a spark cluster – broadcast variables & Accumulators.
Linear Regression with Knime
Reading Time: 5 minutes Linear Regression is perhaps one of the most well known and well -understood algorithms in Statistics and Machine Learning. It tries to find a relationship between the independent and dependent continuous variables by determining a linear equation of the form Y = b0 + b1*x1 + b2*x2 + ….. Here, the x values represent the independent variables, b values are the coefficients of the independent Continue Reading
Data Access with Knime
Reading Time: 5 minutes Knime provides access to various file formats without worrying about the coding behind it. The user just needs to drag and drop an appropriate access node.
Morpheus – Cypher for Spark
Reading Time: 4 minutes Morpheus brings the world of Graph Processing to Spark. It’s Property Graph Model and Cypher language allows easy inclusion of Graph into Spark pipelines
A Little Hands-on with NumPy
Reading Time: 4 minutes NumPy’s built-in methods and concepts like vectorization, broadcasting and indexing allows you to focus on answering questions from your data and not about how to code those solutions. NumPy handles most of that for you.
NumPy – Say Bye to Loops
Reading Time: 5 minutes Numpy draws it’s powers from two major concepts – vectorization and broadcasting, These concepts help Numpy to say good bye to loops and hello to concise coding.
Scale Out with Cluster Sharding
Reading Time: 3 minutes If your actors are distributed across several nodes in the cluster, Cluster Sharding allows you to interact with them without worrying about their physical location and using only their logical identifier. Even if an actor re-locates to a new node, Akka will take care of locating it for you. You just need to send a message to it as if it is located on your local node.
Mocking The Right Way
Reading Time: 3 minutes Mockito is a fun way to perform unit tests, but it’s true potential can only be realised if the underlying layers are designed in such a way that there is no interaction with the internal logic of other components of the application.
Cafeteria Management System with a Twist
Reading Time: 2 minutes System to digitalize manual management of cafeteria.
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