Azure Databricks – Assessment

ISmile Technologies provides solution using Azure data lakes for Data Engineering, Data Science, Analytics & Machine Learning.

Overview

Databricks are widely used and growing fast in technical industries for data analytics and machine learning. Databricks SQL, Databricks Data Science & Engineering, and Databricks Machine Learning are the services we offer to our clients by optimizing Azure Databricks.    

ISmile Technologies analyses clients’ data by leveraging Apache Spark environment, Machine learning models through modern and hi-tech libraries such as Pytorch, TensorFlow, and sci-kit-learn, and support to manage and arrange data using SQL, Scala, R, Python and Java. 

Is your company taking advantage of Databricks ?

Optimized Spark Engine

  • Data Processing with Auto-Scaling and Spark optimized up to 50x performance gains
  • Can host client’s data by autoscaling and transform Raw data into meaningful insights

Machine Learning

  • Pre- configured Environments with frameworks
  • Making predictive models to manage resources and forecast inventory

ML Flow

  • To collaborate with your teammates regarding repositories
  • To Control ML Flow model registry and deployment
  • Central Place to discover and share ML models
  • Use cases in Industries

Optimized Spark Engine

  • Data Processing with Auto-Scaling and Spark optimized up to 50x performance gains 
  • Can host client’s data by autoscaling and transform Raw data into meaningful insights 
  • Machine Learning 

Pre-configured Environments with frameworks

  • Making predictive models to manage resources and forecast inventory 
  • ML Flow 

To collaborate with your teammates regarding repositories

  • To Control ML Flow model registry and deployment 
  • Central Place to discover and share ML models 
  • Use cases in Industries 

Online customer and retail service: 

All the real-time data is managed through the data lakes in Azure Databricks. The basis of data injected into the clusters can be visualized, and analysis can be done. Based on the analysis, organizations set the target clients and harness the full potential of Databricks services.  

Financial Analysis: 

Most companies prefer analyzing available business in the market and the current flow of business by doing financial analysis. To minimize the risk of fraud and accelerate the results, output Databricks helps. AI models using Python and R helps to predict the market positions.  

Our experienced staff help your organization explore and develop data to the required outputs.  

Data Engineering Goals:

  • Transform raw data into meaningful insights   
  • Perform data validation, ETL, and Change data capture  
  • To have everything in cluster memory which is easy to set up and configure

Data Science Goals:

  • To train the model using in-built libraries  
  • Make predictions on data   
  • Increase the security and privacy of data

Assessment Phases

Phase 1: Conduct the scheduled meeting to find out key use cases, discuss on master business presentation and improve techniques as per desired values.  

Phase 2: Set up an environment of Azure Databricks by loading relevant data.   

Phase 3: Systemized the Azure competencies and demonstrated how the Spark environment could help to analyze the data and get desired output using SQL, Python, R, and Java.  

Phase 4: Discuss the machine learning techniques to accelerate the predictive analysis to derive better decisions.  

Phase 5: Present the derived results and outcomes in front of your team; evaluate all the analyses on Tableau or Power BI to showcase the visualization with the real-time data.   

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