In the previous post about Apache Ignite, we learnt how to set up and create either a simple cache or a sql cache, and share the cached data between different nodes. In this post, we will dig a little deeper. We will see what to do if our app crashes because the cached data has disappeared. How could Ignite help us avoid this problem?
Stratio has created a new user interface that allows you to work without writing a single line of code, which means that no programming skills are needed nor to use advanced technologies such as Spark, Scala, HDFS, or Elasticsearch. Developers, Architects, BI engineers, data scientists, business users and IT administrators can create data analytics applications in minutes with a powerful Spark Visual Editor. Welcome to Sparta 2.0, a brand-new version of Sparta born with the forthcoming release of the Stratio Data Centric Platform.
Spark Streaming is one of the most widely used frameworks for real time processing in the world with Apache Flink, Apache Storm and Kafka Streams. However, when compared to the others, Spark Streaming has more performance problems and its process is through time windows instead of event by event, resulting in delay.
This post is about an exciting journey that starts with a problem and ends with a solution. One of the top banks in Europe came to us with a request: they needed a better profiling system.
Imagine a rectangular grid of cells, in which each cell has a value – Either black (dead) or white (alive). And imagine that:
- Any live cell with two or three live neighbors survives for the next generation.
- Any cell with four or more neighbors dies from overpopulation.
- Any cell with one or no neighbors dies from isolation.
- Any dead cell with exactly three neighbors comes to life.
These are the four simple rules of Conway’s Game of Life . You could hardly imagine a simpler set of rules to code on your computer and you wouldn’t expect any interesting result at all, but…
Behold the wonders of its hidden might!
Implicit parameters and conversions are powerful tools in Scala increasingly used to develop concise, versatile tools such as DSLs, APIs, libraries…
When working with Big Data, sometimes it’s useful to remember that powerful products wouldn’t work properly without the tools that build them. It’s possible to start programming in Scala with a few case classes and a bunch of for-comprehensions, but those are only little scratches in a huge ice surface like Scala is. It may not be enough to make your code clean and comprehensible. I’ve been developing with this programming language for almost 4 years, and every day I discover a new feature that surprises me. That acknowledgement, in the end, is the main reason to keep digging deeper into Scala.
Here in Stratio we use Scala massively; we enjoy doing so and the most important thing is: we try to improve our functional-skills every single day. If you really want to learn and soak up every bit of Scala’s powerful functional features try not to learn them all at once, pick one and try to think of parts of your current code where this feature might fit in.