In one of my previous posts, I took you through the steps that I performed to preprocess Criteo dataset used for prediction of the click through rate on Ads. I also trained ML models to predict labels on the testing data set. You can find the post here.
In this post, I will be taking you through the steps that I performed to simulate the process of ML models predicting labels on streaming data.
When you go through the mentioned post, you will find that I used pyspark on DataBricks notebooks to preprocess the Criteo data. I split the preprocessed data into training set and testing set. In order to export the test data to my local machine as a single parquet file, first I saved the training set in the FileStore in one partition as one file using dataFrameName.coalesce(1).write.
I used mleap to export my trained models as a zip file. In order to use mleap, I had to install mleap-spark from maven, mleap from pypi, and mlflow. Then, I copied the model to the FileStore so I can download to my local machine.
Make sure that the version of the mleap-pypi version matches the mleap-maven version. For learning the version of the mleap-pypi installed on DataBricks , you can do the following:
You can learn out the version of the mleap-maven version by looking through the coordinates e.g. ml.combust.mleap:mleap-spark_2.11:0.16.0.
After downloading my models and the testing dataset on my local machine, I had a docker compose up running with Kafka, Zookeeper, Logstash, Elasticsearch and Kibana.
I Developed a producer where I used “pyarrow” library to read the parquet file that has the test dataset. The producer then sends the label (class decision) and the features column to Kafka in a streaming fashion.
#kibana #streaming #kafka #elasticsearch #logstash #data-science
Corona Virus Pandemic has brought the world to a standstill.
Countries are on a major lockdown. Schools, colleges, theatres, gym, clubs, and all other public places are shut down, the country’s economy is suffering, human health is on stake, people are losing their jobs and nobody knows how worse it can get.
Since most of the places are on lockdown, and you are working from home or have enough time to nourish your skills, then you should use this time wisely! We always complain that we want some ‘time’ to learn and upgrade our knowledge but don’t get it due to our ‘busy schedules’. So, now is the time to make a ‘list of skills’ and learn and upgrade your skills at home!
And for the technology-loving people like us, Knoldus Techhub has already helped us a lot in doing it in a short span of time!
If you are still not aware of it, don’t worry as Georgia Byng has well said,
“No time is better than the present”
– Georgia Byng, a British children’s writer, illustrator, actress and film producer.
No matter if you are a developer (be it front-end or back-end) or a data scientist, tester, or a DevOps person, or, a learner who has a keen interest in technology, Knoldus Techhub has brought it all for you under one common roof.
From technologies like Scala, spark, elastic-search to angular, go, machine learning, it has a total of 20 technologies with some recently added ones i.e. DAML, test automation, snowflake, and ionic.
Every technology in Tech-hub has n number of templates. Once you click on any specific technology you’ll be able to see all the templates of that technology. Since these templates are downloadable, you need to provide your email to get the template downloadable link in your mail.
These templates helps you learn the practical implementation of a topic with so much of ease. Using these templates you can learn and kick-start your development in no time.
Apart from your learning, there are some out of the box templates, that can help provide the solution to your business problem that has all the basic dependencies/ implementations already plugged in. Tech hub names these templates as xlr8rs (pronounced as accelerators).
xlr8rs make your development real fast by just adding your core business logic to the template.
If you are looking for a template that’s not available, you can also request a template may be for learning or requesting for a solution to your business problem and tech-hub will connect with you to provide you the solution. Isn’t this helpful 🙂
To keep you updated, the Knoldus tech hub provides you with the information on the most trending technology and the most downloaded templates at present. This you’ll be informed and learn the one that’s most trending.
Since we believe:
“There’s always a scope of improvement“
If you still feel like it isn’t helping you in learning and development, you can provide your feedback in the feedback section in the bottom right corner of the website.
#ai #akka #akka-http #akka-streams #amazon ec2 #angular 6 #angular 9 #angular material #apache flink #apache kafka #apache spark #api testing #artificial intelligence #aws #aws services #big data and fast data #blockchain #css #daml #devops #elasticsearch #flink #functional programming #future #grpc #html #hybrid application development #ionic framework #java #java11 #kubernetes #lagom #microservices #ml # ai and data engineering #mlflow #mlops #mobile development #mongodb #non-blocking #nosql #play #play 2.4.x #play framework #python #react #reactive application #reactive architecture #reactive programming #rust #scala #scalatest #slick #software #spark #spring boot #sql #streaming #tech blogs #testing #user interface (ui) #web #web application #web designing #angular #coronavirus #daml #development #devops #elasticsearch #golang #ionic #java #kafka #knoldus #lagom #learn #machine learning #ml #pandemic #play framework #scala #skills #snowflake #spark streaming #techhub #technology #test automation #time management #upgrade
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
Predictive modeling in data science is used to answer the question “What is going to happen in the future, based on known past behaviors?” Modeling is an essential part of data science, and it is mainly divided into predictive and preventive modeling. Predictive modeling, also known as predictive analytics, is the process of using data and statistical algorithms to predict outcomes with data models. Anything from sports outcomes, television ratings to technological advances, and corporate economies can be predicted using these models.
#big data #data science #predictive analytics #predictive analysis #predictive modeling #predictive models
Welcome back folks to this blog series of Spark Structured Streaming. This blog is the continuation of the earlier blog “Internals of Structured Streaming“. And this blog pertains to Stateful Streaming in Spark Structured Streaming. So let’s get started.
Let’s start from the very basic understanding of what is Stateful Stream Processing. But to understand that, let’s first understand what Stateless Stream Processing is.
In my previous blogs of this series, I’ve discussed Stateless Stream Processing.
You can check them before moving ahead – Introduction to Structured Streaming and Internals of Structured Streaming
#analytics #apache spark #big data and fast data #ml #ai and data engineering #scala #spark #streaming #streaming solutions #tech blogs #stateful streaming #structured streaming
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management