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 Data Science Dojo is offering Locust for FREE on Azure Marketplace packaged with pre-configured Python interpreter and Locust web server for load testing. 

 

Why and when do we perform testing? 

Testing is an evaluation and confirmation that a software application or product performs as intended. The purpose of testing is to determine whether the application satisfies business requirements and whether the product is market ready. Applications can be subjected to automated testing to see if they meet the demands. Scripted sequences are used in this method of software testing, and testing tools carry them out. 

The merits of automated testing are: 

  • Bugs can be avoided 
  • Development costs can be reduced 
  • Performance can be improved till requirement 
  • Application quality can be enhanced 
  • Development time can be saved 

Testing is usually the last phase of the SDLC (Software Development Life Cycle)  

What is load testing and why choose Locust?  

Performance testing is one of several types of software testing. Load testing is an example of performance testing to evaluate performance under real-life load conditions. It involves the following stages: 

  • Define crucial metrics and scenarios 
  • Plan the test load model 
  • Write test scenarios 
  • Execute test by swarming load 
  • Analyze the test results 

It is a modern load testing framework. The major reason senior testers prefer it over other tools like JMeter is because it uses an event-based approach for testing rather than thread based. This results in less consumption of resources and thus saves costs. 

Pro Tip: Join our 6-months instructor-led Data Science Bootcamp to master data science skills 

Challenges faced by QA teams  

Before such feasible testing tools, the job of testing teams was not much easier as it is now. Swarming a large number of users to direct as a load on a website was expensive and time-consuming.  

Apart from this, monitoring the testing process in real time was not prevalent either. Complete analytics were usually drawn after the whole testing process concludes, which again required patience. 

The testers needed a platform through which they can evaluate quality of product and its compliance with the specified requirements under different loads without the prolonged wait and high expense. 

Working of Locust 

Locust is an open-source web-based load testing tool. It is based on python and is used to evaluate the functionality and behavior of the web application. For the quality assurance process in any business, load testing is an extremely critical element to assure that the website remains up during traffic influx as it will eventually contribute to the success of the company. Through Locust, web testers can determine the potential of the website to withstand the number of concurrent users. With the power of python, you can develop a set of test scenarios and functions that imitate many users and can observe performance charts on web UI. 

 

Locust file
Figure 1: A sample locustfile.py 

 

The self.client.get function points to the pages of a website that you want to target. You can find this code file and further breakdown here. The host domain, users and the spawn rate for the load testing are supplied at the web interface. After running the locust command, the web server is started at 8089. 

 

locust web interface
Figure 2: Locust web interface

 

It also allows you to capture different metrics during the testing process in real-time. 

 

graphs with metrics
Figure 3: Graphs with metrics visualizations

 

Key characteristics of Locust 

 

  • An interactive user-friendly web UI is started after executing the file through which you can perform load testing 
  • Locust is an open-source load-testing tool. It is extremely useful for web app testers, QA teams and software testing managers 
  • You can capture various metrics like response time, visualized in charts in real-time as the testing occurs 
  • Achieve increased throughput and high availability by writing test codes in pre-configured python interpreter 
  • You can easily scale up the number of users for extensive production level load testing of web applications 

 

What Data Science Dojo provides 

 

Locust instance packaged by Data Science Dojo comes with a pre-configured python interpreter to write test files, and a Locust web UI server to generate the desired amount of load at specific rates without the burden of installation.  

Features included in this offer:  

  • VM configured with Locust application which can start a web server with rich UX/UI 
  • Provides several interactive metrics graphs to visualize the testing results 
  • Provides real-time monitoring support 
  • Ability to download requests statistics, failures, exceptions, and test reports 
  • Feature to swarm multiple users at the desired spawn rate 
  • Support for python language to write complex workflows 
  • Utilizes event-based approach to use fewer resources 

Through Locust, load testing has been easier than ever. It has saved time and cost for businesses as QA engineers and web testers can perform testing now with few clicks and few lines of easy code. 

 

 

Conclusion 

 

Locust can be used to test any web application. By swarming many clients spawning at a specific rate, the functionality of a website can be assured that it can manage concurrent users. To achieve extensive load testing, you can use multi-cores on Azure Virtual Machine. Also, the Its web interface calculates metrics for every test run and visualizes them as well. This might slow down the server if you have hundreds upon hundreds of active test units requesting multiple pages. The CPU and RAM usage may also be affected but through Azure Virtual Machine this problem is taken care of. 

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We are adding a free Locust application dedicated specifically for testing operations on Azure Marketplace. Now hurry up install this offer by Data Science Dojo, your ideal companion in your journey to learn data science!  

 

Click on the button below to head over to the Azure Marketplace and deploy Locust for FREE by clicking on “Try now” 

CTA - Try now 

Note: You will have to sign up to Azure, for free, if you do not have an existing account. 

November 26, 2022

Data Science Dojo is offering Apache Druid for FREE on Azure Marketplace packaged with a pre-configured web environment of Druid with support of various data sources. 

What is data ingestion? 

Data ingestion is the method involved with shipping information from at least one source to an objective site for additional handling and examination. This information can begin from a scope of sources, including data lakes, IoT gadgets, on-premises data sets, and other applications, and arrive in various environments, for example, cloud warehouse or our very own Druid data store. 

OLAP 

Online Analytical Processing (OLAP) is a method for quickly responding to multidimensional analytical questions in computing. OLAP frameworks are usually utilized in numerous BI and data science programs. It involves ingesting data in real-time, whether it’s streaming or in batches, for drawing analytics. OLAP systems usually maintain a data warehouse having redundancy along with maintaining time-series of datasets. They require customized queries to be computed at fast speeds. 

 

Pro Tip: Join our 6-months instructor-led Data Science Bootcamp to master data science & engineering 

Backend services of Apache Druid  

  1. Middle Manager: This process is responsible for ingesting the data 
  2. Broker: This process is responsible for retrieving queries from external clients 
  3. Coordinator: It assigns segments to specific nodes 
  4. Overlord: It assigns ingestion tasks to middle managers 
  5. Historical: It handles the storage and querying of data 
  6. Router: Optional component to provide single API gateway for coordinators, overlords and brokers 

Obstacles for data engineers & developers 

Collection and maintenance of data from different sources was a hectic task for data engineers and developers. The organization of schema and its monitoring was another challenge in case of huge data. The requirement to response efficiently to complex OLAP queries and any sort of quick calculation was a nightmare. 

In this scenario, a unified environment to deal with the ad-hoc queries, management of different data sets, keeping the time-series of data and quick data ingestions from various sources all from one place would be enough to tackle the mentioned challenges. 

Methodology of Apache Druid  

Apache Druid is an interactive real-time database backend environment for ingesting, maintaining, and segmenting data from a variety of sources either streaming or in batches, thus making it flexible. It is a scalable distributed system with parallel processing for queries and has a column-based structure for storing datasets, indicating the properties of each ingestion.

Druid stores the data safely in deep storage and provides indexing and time-based partitioning for faster filtering and searching performance. Users can query the ingested datasets with Druid’s optimized SQL engine. It also provides automatic summarization and algorithmic approximation of data. 

druid-architecture
Druid Architecture (Picture Courtesy: https://druid.apache.org/docs/latest/design/architecture.html ) 

 

Major features   

  • Apache Druid has a fast and optimized user interface. Druid UI makes it easy to supervise, refresh and troubleshoot your datasets. The column-oriented organization provides ease of control to the users 
  • Any ingested data can be subjected to queries with the help of an in-browser SQL editor. It delivers the results with low latency 
  • It is an open-source tool. Developers, data engineers, DevOps, companies focusing on web and mobile analytics, solutions architects who want to monitor network performance, and anyone interested in data science can use this offer 
  • Druid provides the feature of maintaining logs of each activity. In case of failure of any operation, the logs are updated, and the user can check them on the same web server 
  • You can monitor the status of your datasets oriented in a column via the web server 

 

What does Data Science Dojo provide?  

Apache Druid instance packaged by Data Science Dojo serves as a pre-configured data store for managing and monitoring ingested data along with SQL support to query data without the burden of installation. It offers efficient storage, quick sifting on dimensions of data, and querying of data at a sub-second normal reaction time. It supports a variety of data sources to ingest data from. 

Features included in this offer:  

  • A Druid service that is easily accessible from the web, having a rich user interface 
  • Easy to operate and user friendly 
  • In-browser SQL coding environment to query ingested data sets 
  • Low latency automated data aggregations and approximations using algorithms 
  • Quick responsiveness and high uptime 
  • Time-based data partitioning 
  • Feature of schema configuration and data tuning at the time of ingestion 

Our instance of Apache Druid supports the following data sources: 

  • Apache Kafka 
  • HDFS 
  • HTTP(s) 
  • Local disk 
  • Azure Event Hub 
  • Paste Data 
  • Other custom sources 

By specifying credentials and adding extensions you can also ingest from : 

  • Azure Data Lake 
  • Google Cloud Storage 
  • Amazon S3 & Kinesis 

Conclusion 

Apache Druid is majorly used for OLAP systems because of its time series data ingestion, and the way the services perform indexing, and response to queries in real-time. It has a flexible and fault-tolerant architecture. When coupled with Microsoft cloud services, responsiveness and processing speed outperform their traditional counterparts because data-intensive computations aren’t performed locally, but in the cloud. 

Install the Apache Druid offer now from the Azure Marketplace by Data Science Dojo, your ideal companion in your journey to learn data science! 

 

Click on the button below to head over to the Azure Marketplace and deploy Apache Druid for FREE by clicking on “Try now”.   

CTA - Try now

Note: You’ll have to sign up to Azure, for free, if you do not have an existing account.   

 

November 11, 2022

Data Science Dojo is offering Metabase for FREE on Azure Marketplace packaged with web accessible Metabase: Open-Source server. 

Metabase query
Metabase query

 

Introduction 

Organizations often adopt strategies that enhance the productivity of their selling points. One strategy is to utilize the prior business data to identify key patterns regarding any product and then take decisions for it accordingly. However, the work is quite hectic, costly, and requires domain experts. Metabase has bridged that gap of skillset. Metabase provides marketing and business professionals with an easy-to-use query builder notebook to extract required data and simultaneously visualize it without any SQL coding, with just a few clicks. 

What is Metabase and its question? 

Metabase is an open-source business intelligence framework that provides a web interface to import data from diverse databases and then analyze and visualize it with few clicks. The methodology of Metabase is based on questions and the answers to them. They form the foundation of everything else that it provides. 

           

A question is any kind of query that you want to perform on a data. Once you are done with the specification of query functions in the notebook editor, you can visualize the query results. After that you can save this question as well for reusability and turn it into a data model for business specific purposes. 

Pro Tip: Join our 6-months instructor-led Data Science Bootcamp to become expert at data science & analytics skillset 

Challenges for businesses  

For businesses that lack expert analysts, engineers and substantial IT department, it was costly and time-consuming to hire new domain experts or managers themselves learn to code and then explore and visualize data. Apart from that, not many pre-existing applications provide diverse data source connections which was also a challenge. 

In this regard, a straightforward interactive tool that even newbies could adapt immediately and thus get the job done would be the most ideal solution. 

Data analytics with Metabase  

Metabase concept is based on questions which are basically queries and data models (special saved questions). It provides an easy-to-use notebook through which users can gather raw data, filter it, join tables, summarize information, and add other customizations without any need for SQL coding.

Users can select the dimensions of columns from tables and then create various visualizations and embed them in different sub-dashboards. Metabase is frequently utilized for pitching business proposals to executive decision-makers because the visualizations are very simple to achieve from raw data. 

 

visualization on sample data
Figure 1: A visualization on sample data 

 

A visualization on sample data 
Figure 2:  Query builder notebook

 

Major characteristics 

  • Metabase delivers a notebook that enables users to select data, join with other tables, filter, and other operations just by clicking on options instead of writing a SQL query 
  • In case of complex queries, a user can also use an in-built optimized SQL editor 
  • The choice to select from various data sources like PostgreSQL, MongoDB, Spark SQL, Druid, etc., makes Metabase flexible and adaptable 
  • Under the Metabase admin dashboard, users can troubleshoot the logs regarding different tasks and jobs 
  • Has the ability to enable public sharing. It enables admins to create publicly viewable links for Questions and Dashboards  

What Data Science Dojo has for you  

Metabase instance packaged by Data Science Dojo serves as an open-source easy-to-use web interface for data analytics without the burden of installation. It contains numerous pre-designed visualization categories waiting for data.

It has a query builder which is used to create questions (customized queries) with few clicks. In our service users can also use an in-browser SQL editor for performing complex queries. Any user who wants to identify the impact of their product from the raw business data can use this tool. 

Features included in this offer:  

  • A rich web interface running Metabase: Open Source 
  • A no-code query building notebook editor 
  • In-browser optimized SQL editor for complex queries 
  • Beautiful interactive visualizations 
  • Ability to create data models 
  • Email configuration and Slack support 
  • Shareability feature 
  • Easy specification for metrics and segments 
  • Feature to download query results in CSV, XLSX and JSON format 

Our instance supports the following major databases: 

  • Druid 
  • PostgreSQL 
  • MySQL 
  • SQL Server 
  • Amazon Redshift 
  • Big Query 
  • Snowflake 
  • Google Analytics 
  • H2 
  • MongoDB 
  • Presto 
  • Spark SQL 
  • SQLite 

Conclusion  

Metabase is a business intelligence software and beneficial for marketing and product managers. By making it possible to share analytics with various teams within an enterprise, Metabase makes it simple for developers to create reports and collaborate on projects. The responsiveness and processing speed are faster than the traditional desktop environment as it uses Microsoft cloud services. 

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We are therefore adding a free Metabase server dedicated specifically for Data Analytics operations on Azure Market Place. Hurry up and install this offer by Data Science Dojo, your ideal companion in your journey to learn data science!  

Click on the button below to head over to the Azure Marketplace and deploy Metabase for FREE by clicking on “Get it now”. 

CTA - Try now

Note: You’ll have to sign up to Azure, for free, if you do not have an existing account. 

November 5, 2022

Data Science Dojo is offering Countly for FREE on Azure Marketplace packaged with web accessible Countly Server. 

Purpose of product analytics  

Product analytics is a comprehensive collection of mechanisms for evaluating the performance of digital ventures created by product teams and managers. 

Businesses often need to measure the metrics and impact of their products, for e.g., how the audience perceives their product like how many visitors are reading a particular page or clicking on a specific button. This gives an insight into what future decisions need to be taken regarding any product. Whether it should be modified? or removed? or kept as it is? Countly has made this work easier by providing a centralized web analytics environment to track the user engagement with a product along with monitoring its health.  

 

Pro Tip: Join our 6-months instructor-led Data Science Bootcamp to become expert at data science & analytics skillset 

Challenges for individuals  

Many platforms require developers for coding to visualize analytics which is not only time consuming but also come at a cost. At the application level, having an app crash leaves anyone in shock, and that is followed by a hectic task of determining the root cause of the problem which is time-consuming. At the corporate level, the current and past data needs to be analyzed appropriately for the future strength of the company and that requires robust analysis easily acquired by anyone which was a challenge faced by many organizations  

Countly analytics 

Countly enables users to monitor and analyze the performance of their applications irrespective of the platform in real-time. It can compile data from numerous sources and presents it in a manner that makes it easier for business analysts and managers to evaluate app usage and client behavior. It offers a customizable dashboard with the freedom to innovate and improve your products in order to meet important business and revenue objectives while also ensuring privacy by design. It is a world leader in product analytics because it tracks more than 1.5 billion unique identities on more than 16,000 applications and more than 2,000 servers worldwide. 

 

Analytics based technology - countly
Figure 1: Analytics based on type of technology

 

 

Analytics based on user activity - Countly
Figure 2: Analytics based on user activity

 

 

Figure 3: Analytics based on views - Countly
Figure 3: Analytics based on views

 

Major characteristics 

  • Interactive web interface: User-friendly web environment with customizable dashboards for easy accessibility along with pre-designed metrics and visualizations 
  • Platform-independent: Supports web analytics, mobile app analytics, and desktop application analytics for macOS and Windows 
  • Alerts and email reporting: Ability to receive alerts based on the metric changes and provides custom email reporting 
  • Users’ role and access manager: Provides global administrators the ability to manage users, groups, and their roles and permissions 
  • Logs Management: Maintains server and audit logs on the web server regarding user actions on data 

What Data Science Dojo has for you  

Countly Server packaged by Data Science Dojo provides a web analytics service that provides insights about your product in real-time, no matter if it’s a web application or mobile app, or even desktop application without the burden of installation. It comes with numerous pre-configured metrics and visualization templates to import data and observe trends. It’s helpful for businesses to identify the application usage and determine the client response to the apps.  

Features included in this offer:  

  • A VM configured with Countly Server: Community Edition accessible from a web browser 
  • Ability to track user analytics, user loyalty, session analytics, technology, and geo insights  
  • Easy-to-use customizable dashboard 
  • Logs manager 
  • Alerting and reporting feature 
  • User permissions and roles manager 
  • Built-in Countly DB viewer 
  • Cache management 
  • Flexibility to define data limits 

Conclusion  

Countly provides the feasibility to analyze data in real-time. It is highly extensible and possesses various features to manage different operations like alerting, reporting, logging, job management, etc. The analytics throughput can be increased by using multi-cores on Azure Virtual Machine. Also, Countly can handle different platform applications at once. This might slow down the server if you have thousands upon thousands of active client requests on different applications. The CPU and RAM usage may also be affected but through Azure Virtual Machine all these problems are taken care of. 

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We are therefore adding a free Countly Server dedicated specifically for Data Analytics operations on Azure Market Place. Hurry up and install this offer by Data Science Dojo, your ideal companion in your journey to learn data science! 

Click on the button below to head over to the Azure Marketplace and deploy Countly for FREE by clicking on “Try now”. 

CTA - Try now

Note: You’ll have to sign up to Azure, for free, if you do not have an existing account. 

 

October 26, 2022

Data Science Dojo is offering RStudio for FREE on Azure Marketplace packaged with a pre-installed running version of R alongside other language backends to simplify Data Science. 

 

What is data science? 

 

Data Science is one of the quickest-growing areas of work in the industry. According to Harvard Business Review, it’s regarded as the “sexiest job of the 21st century”. 

Data science joins math and measurements, programming, refined analyses, machine learning and AI to reveal significant knowledge concealed in an association’s dataset. These understandings can be utilized to direct businesses in planning and decision making. The lifecycle of Data Science involves data collection (ingestion), data pre-processing and wrangling, predictive data analysis via machine learning and finally communication of outcomes for future strategies. 

 

Pro Tip: Join our 6-months instructor-led Data Science Bootcamp to master data science. 

 

Challenges faced by developers 

 

Individuals who were learning or pursuing Data Science and Machine Learning through R found it difficult to code and develop models using only a terminal or command line interface. Developers who wanted to perform extensive high powered ML operations but didn’t have enough computation power to do it locally was also another challenge.  

In these circumstances an interactive environment configured with R can help the users in gaining hands-on experience with machine learning, data analysis and other statistical operations. 

Working with RStudio 

 

RStudio is an open-source tool that gives you an effortless coding IDE in the cloud with a pre-installed R programming language to start your data mining and analytics work. It is integrated with a set of modules that make code development, scientific computing, and graphical jobs to be more productive and easier. This tool allows developers to perform a variety of technical tasks such as predictive modeling, clustering, multivariate querying, stock market rate, spam filtering, recommendation systems, malware, and anomaly detection, image recognition, and medical diagnosis. 

 

Rstudio -potential for data science
Web interface of RStudio Server executing a demo R function

 

Key attributes 

 

  • Provides an in-browser coding environment with syntax suggestions, autocomplete code feature and smart indentation 
  • Provides the user with an easy-to-use free coding platform accessible at the local web server, powered by Azure machines 
  • Apart from the primary built of R, RStudio has support for other famous interpreters as well such as Python, SQL, HTML, CSS, JS, C, Quarto and a few others 
  • In-built debugging functionality by toggling breakpoints to detect and eradicate the issues or fix them quickly 
  • As the computations are carried on Microsoft’s cloud servers, there is no memory or performance pressure on the company’s storage devices 
  • In order to optimize the workload, the RAM and compute power can be scaled accordingly, thanks to Azure services 

 

What Data Science Dojo has for you 

 

The RStudio instance packaged by Data Science Dojo provides an in-browser coding environment with a running version of R pre-deployed in it, reducing the burden of installation. With an interactive user-friendly GUI-based application, developers can perform Machine Learning tasks with comfort and flexibility.  

  • A browser based RStudio environment up and running with R pre-deployed 
  • Convenient accessibility and navigation 
  • Ability to work with different language scripts simultaneously 
  • Rich graphics and interactive environment 
  • Support for git and version control 
  • Code consoles to run code interactively, with full support for rich output 
  • Integrated R documentation and user help 
  • Readily available cheat sheets to get started 

Our instance supports the following backends: 

  • R 
  • Python 
  • HTML 
  • CSS 
  • JavaScript 
  • Quarto 
  • C 
  • SQL 
  • Shell 
  • Markdown and Header files 

 

Conclusion 

 

RStudio provides customers with an easy-to-use environment to gain hands-on experience with Machine Learning and Data Science. The responsiveness and processing speed are much better than the traditional desktop environment as it uses Microsoft cloud services. It comes with built-in support for git and version control.

Several variants of the R script can be executed in RStudio. It allows users to work on a variety of language backends at the same time with smart observability of variables and values side by side. The documentation and user support are incorporated into the tool to make it easy for developers to code. 

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We are therefore adding a free RStudio instance dedicated specifically to Machine Learning and Data Science on Azure Marketplace. Now hurry up and avail this offer by Data Science Dojo, your ideal companion in your journey to learn data science! 

 

Click on the button below to head over to the Azure Marketplace and deploy Rstudio for FREE by clicking on “Get it now”.  

CTA - Try now

Note: You’ll have to sign up to Azure, for free, if you do not have an existing account.

October 21, 2022

Data Science Dojo is offering Apache Superset for FREE on Azure Marketplace packaged with pre-installed SQL lab and interactive visualizations to get started. 

 

What is Business Intelligence?  

 

Business Intelligence (BI) depends on the idea of utilizing information to perform activities. It expects to give business pioneers noteworthy bits of knowledge through data handling and analytics. For instance, a business breaks down the KPIs (Key Performance Indicators) to distinguish its benefits and shortcomings. Hence, the decision-makers can conclude in which department the organization can work to increase efficiency.  

Recently two elements in BI have resulted in sensational enhancements in metrics like speed and proficiency. The two elements include:  

 

  • Automation  
  • Data Visualization  

 

Apache Superset widely focuses on the latter model which has changed the course of business insights.  

 

But what were the challenges faced by analysts before there were popular exploratory tools like Superset?  

 

Pro Tip: Join our 6-months instructor-led Data Science Bootcamp to master data science. 

 

Challenges of Data Analysts

 

Scalability, framework compatibility, and absence of business-explicit customization were a few challenges faced by data analysts. Apart from that exploring petabytes of data and visualizing it would cause the system to collapse or hang at times.  

In these circumstances, a tool having the ability to query data as per business needs and envision it in various diagrams and plots was required. Additionally, a system scalable and elastic enough to handle and explore large volumes of data would be an ideal solution.  

 

Data Analytics with Superset  

 

Apache Superset is an open-source tool that equips you with a web-based environment for interactive data analytics, visualization, and exploration. It provides a vast collection of different types of vibrant and interactive visualizations, charts, and tables. It can customize the layouts and the dynamic dashboard elements along with quick filtering, making it flexible and user-friendly. Apache Superset is extremely beneficial for businesses and researchers who want to identify key trends and patterns from raw data to aid in the decision-making process.  

 

Sales analytics - Apache superset
Video Game Sales Analytics with different visualizations

 

 

It is a powerhouse of SQL as it not only allows connection to several databases but also provides an in-browser SQL editor by the name SQL Lab  

SQL lab - Apache superset
SQL Lab: an in-browser powerful SQL editor pre-configured for faster querying

 

Key attributes  

 

  • Superset delivers an interactive UI that enriches the plots, charts, and other diagrams. You can customize your dashboard and canvas as per requirement. The hover feature and side-by-side layout make it coherent  
  • An open-source easy-to-use tool with a no-code environment. Drag and drop and one-click alterations make it more user-friendly  
  • Contains a powerful built-in SQL editor to query data from any database quickly  
  • The choice to select from various databases like Druid, Hive, MySQL, SparkSQL, etc., and the ability to connect additional databases makes Superset flexible and adaptable  
  • In-built functionality to create alerts and notifications by setting specific conditions at a particular schedule  
  • Superset provides a section about managing different users and their roles and permissions. It also has a tab for logging the ongoing events  

 

What does Data Science Dojo have for you  

 

Superset instance packaged by Data Science Dojo serves as a web-accessible no-code environment with miscellaneous analysis capabilities without the burden of installation. It has many samples of chart and dataset projects to get started. In our service users can customize dashboards and canvas as per business needs.

It comes with drag-and-drop feasibility which makes it user-friendly and easy to use. Users can create different visualizations to detect key trends in any volume of data.  

 

What is included in this offer:  

 

  • A VM configured with a web-accessible Superset application  
  • Many sample charts and datasets to get started  
  • In-browser optimized SQL editor called SQL Lab  
  • User access and roles manager  
  • Alert and report feature  
  • Feasibility of drag and drop  
  • In-build functionality of event logging  

 

Our instance supports the following major databases:  

 

  • Druid  
  • Hive  
  • SparkSQL  
  • MySQL  
  • PostgreSQL  
  • Presto  
  • Oracle  
  • SQLite  
  • Trino  
  • Apart from these any data engine that has Python DB-API driver and a SQL Alchemy dialect can be connected  

 

Conclusion  

 

Efficient resource requirement for exploring and visualizing large volumes of data was one of the areas of concern when working on traditional desktop environments. The other area of concern includes the ad-hoc SQL querying of data from different database connections. With our Superset instance, both concerns are put to rest.

When coupled with Microsoft cloud services and processing speed, it outperforms its traditional counterparts since data-intensive computations aren’t performed locally but in the cloud. It has a lightweight semantic layer and is designed as a cloud-native architecture.  

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We are therefore adding a free Superset instance dedicated specifically to Data Science & Analytics on Azure Marketplace. Now hurry up and avail this offer by Data Science Dojo, your ideal companion in your journey to learn data science!  

 

Click on the button below to head over to the Azure Marketplace and deploy Apache Superset for FREE by clicking on “Get it now”. 

 

Superset

 

Note: You’ll have to sign up to Azure, for free, if you do not have an existing account. 

 

 

 

 

 

 

 

October 17, 2022

Data Science Dojo is offering SnowSQL for FREE on Azure Marketplace packaged with pre-configured CLI for data manipulation at Snowflake warehouse 

 

What is Snowflake? 

Snowflake is a cloud computing-based data cloud platform. It is a user-friendly data warehousing product that supports both ETL and ELT functionalities. It supports multiple data workloads from data warehouses and data lakes to enable data storage, data engineering, processing, and analytics. Further, using Snowflake can help you with better data warehouse jobs in the near future.

It is relatively new, flexible, easier and provides a pure cloud SQL based warehouse. It is not created upon any database tech and has a high affordability rate. 

Challenges for developers 

Execution issues while attempting to load and query information, shortcomings in dealing with a variety of data, absence of central source causing conflicting corrupt data, and unfortunate data sharing were a few big obstacles encountered by data engineers and developers to look forward to. 

Therefore, a data cloud warehouse capable of storing variable-length records at vast scale maybe having some fault tolerance or availability or fast processing can reduce the task overhead by a big margin. Not to forget, the data able to be transformed using any standard open-source language would suffice. 

Snowflake: SnowSQL 

Using data engineers’ one of the favorite languages, SQL, developers can now load, transform and unload data at their Snowflake cloud. SnowSQL is an interactive command line scripting-cum-query tool that allows users to perform DML and DDL operations at the CLI level. It is produced with increased protection standards and has strong integration with Snowflake core architecture.

With this tool, you can connect to your Snowflake account and configure your databases, schemas, and warehouses using simple SQL. Users can import existing databases of the data cloud into local machines and then perform various transform operations. It also allows you to unload or dump your transformed data back into the database containers. 

Snowflake architecture - SnowSQL
Snowflake architecture – Data Science Dojo

What we provide 

Snowflake: SnowSQL packaged by Data Science Dojo serves as a pre-installed CLI environment for the Snowflake data cloud so it provides an ease to the developers to perform SQL operations on the data from the warehouse without the burden of installation. Offer listed by Data Science Dojo, provides the following elements: 

  • A Command Line Interface (CLI) installed with SnowSQL which can be connected to your Snowflake account 
  • Support for standard SQL 
  • Robust integrations 
  • Ability load and unload data having CSV, XML, JSON, Avro etc formats 
  • Includes syntax highlighting and auto-complete 

Significant characteristics of SnowSQL 

  • Centralization and Democratization: Snowflake combines warehouses and data lakes into a focal data storage and democratizes it to enable clients for better processing and analytics 
  • Smart Data Handling: Snowflake is able to manage exponential volumes, variety, and velocity of data. Using SnowSQL we can perform ETL and ELT operations using ANSI SQL 
  • Secure and Fault Tolerant: SnowSQL secures connections to Snowflake using TLS with OCSP checks. Your data is available even if one or more nodes go down as source is fault-tolerant 
  • Recovery and Time Travel: The undrop table command is unique as it restores the table back again. The time travel feature allows the recovery of the original version of an object to reverse the updates 
  • High Elastic Performance: With SnowSQL you create multiple virtual warehouses of variable sizes which ensures your ETL process go smoothly 

Conclusion 

SnowSQL leverages the power of Azure services and Snowflake Cloud to load and process large volumes of data with continuous availability, high scalability, and data distribution from the comfort of CLI. In this way, Azure increases the fault tolerance of Snowflake clusters.

The power of Azure ensures maximum performance and high throughput for Snowflake nodes by providing a low latency network. Since SnowSQL mirrors Snowflake which stores and computes data, the elastic nature of the cloud will allow it to load data faster or run a high volume of queries. 

Recovery of data has never been this easy. It also provides optimized query parsing and strong integration. Install the SnowSQL offer now from the Azure Marketplace by Data Science Dojo, your ideal companion in your journey to learn data science! 

Click on the button below to head over to the Azure Marketplace and deploy SnowSQL for FREE by clicking on “Get it now”. 

 

SnowSQL Package

Note: You’ll have to sign up to Azure, for free, if you do not have an existing account. 

 

 

October 7, 2022

Data Science Dojo is offering DBT for FREE on Azure Marketplace packaged with support for various data warehouses and data lakes to be configured from CLI. 

 

What does DBT stands for? 

Traditionally, data engineers had to process extensive data available at multiple data clouds in the same available cloud environments. The next task was to migrate the data and then transform it as per the requirements, but Data migration was a task not easy to do so. DBT short for Data Build Tool, allows the analysts and engineers to manipulate massive amounts of data from various significant cloud warehouses to be processed reliably at a single workstation using modular SQL. 

It is basically the “T” in ELT for data transformation in diverse data warehouses. 

 

ELT vs ETL – Insights of both terms

Now what do these two terms mean? Have a look at the table below: 

 

ELT 

ETL 

1.  Stands for Extraction Load Transform  Stands for Extraction Transform Load 
2.  Supports structured, unstructured, semi structured and raw type of data  Requires relational and structured dataset 
3.  New technology, so it’s difficult to find experts or to create data pipelines  Old process, used for over 20 years now 
4.  Dataset is extracted from sources and warehoused in the destination and then transformed  After extraction, data is brought into the staging area where’s its transformed and then loaded into target system 
5.  Quick data loading time because data is integrated at target system once and then transformed  Takes more time as it’s a multistage process involving a staging area for transformation and twice loading operations 

 

Use cases for ELT 

Since dbt relates closely to ELT process, let’s discuss its use cases: 

  • Associations with huge volumes of information: Meteorological frameworks like weather forecasters gather, examine and utilize a lot of information consistently. Organizations with enormous exchange volumes additionally fall into this classification. The ELT process considers faster exchange of data 
  • Associations needing quick accessibility: Stock trades produce and utilize a lot of data continuously, where postponements can be destructive. 

 

Challenges for Data Build Tool (DBT)

Data distributed across multiple data centers and the ability to transform those volumes at a single place was a big challenge. 

Then testing and documenting the workflow was another problem. 

Therefore, an engine that could cater to the multiple disjointed data warehouses for data transformation would be suitable for the data engineers. Additionally, testing the complex data pipeline with the same agent would do wonders. 

Working of DBT

Data Build Tool is a partially open-source platform for transforming and modeling data obtained from your data warehouses all in one place. It allows the usage of simple SQL to manipulate data acquired from different sources. Users can document their files and can generate DAG diagrams thereby identifying the lineage of workflow using dbt docs. Automated tests can be run to detect flaws and missing entries in the data models as well. Ultimately, you can deploy the transformed data model to any other warehouse. DBT serves pleasantly in the cutting-edge information stack and is considered cloud agnostic meaning it operates with several significant cloud environments. 

 

Analytics engineering DBT

(Picture Courtesy: https://www.getdbt.com/

 

 Important aspects of DBT

  • DBT enables data analysts with the feasibility to take over the task of data engineers. With modular SQL at hand, analysts can take ownership of data transformation and eventually create visualizations upon it 
  • It’s cloud agnostic which means that DBT can handle multiple significant cloud environments with their warehouses such as BigQuery, Redshift, and Snowflake to process mission-critical data 
  • Users can maintain a profile specifying connections to different data sources along with schema and threads 
  • Users can document their work and can generate DAG diagrams to visualize their workflow 
  • Through the snapshot feature, you can take a copy of your data at any point in time for a variety of reasons such as tracing changes, time intervals, etc. 

 

What Data Science Dojo has for you 

DBT instance packaged by Data Science Dojo comes with pre-installed plugins which are ready to use from CLI without the burden of installation. It provides the flexibility to connect with different warehouses, load the data, transform it using analysts’ favorite language – SQL and finally deploy it to the data warehouse again or export it to data analysis tools. 

  • Ubuntu VM having dbt Core installed to be used from Command Line Interface (CLI) 
  • Database: PostgreSQL 
  • Support for BigQuery 
  • Support for Redshift 
  • Support for Snowflake 
  • Robust integrations 
  • A web interface at port 8080 is spun up by dbt docs to visualize the documentation and DAG workflow 
  • Several data models as samples are provided after initiating a new project 

This dbt offer is compatible with the following cloud providers: 

  • GCP 
  • Snowflake 
  • AWS 

 

Disclaimer: The service in consideration is the free open-source version which operates from CLI. The paid features as stated officially by DBT are not endorsed in this offer. 

Conclusion 

Incoherent sources, data consistency problems, and conflicting definitions for measurements and enterprise details lead to disarray, excess endeavors, and unfortunate data being dispersed for decision-making. DBT resolves all these issues. It was built with version control in mind. It has enabled data analysts to take on the role of data engineers. Any developer with good SQL skills is able to operate on the data – this is in fact the beauty of this tool. 

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. Therefore, to enhance your data engineering and analysis skills and make the most out of this tool, use the Data Science Bootcamp by Data Science Dojo, your ideal companion in your journey to learn data science! 

Click on the button below to head over to the Azure Marketplace and deploy DBT for FREE by clicking on “Get it now”. 

 Try now - CTA

Note: You’ll have to sign up to Azure, for free, if you do not have an existing account. 

September 29, 2022

Data Science Dojo is offering Apache Zeppelin for FREE on Azure Marketplace packaged with pre-installed interpreters and backends to make Machine Learning easier than ever. 

Introduction 

How cumbersome and tiring it is to install different tools to perform your desired ML tasks and then look after the integration and dependency issues. Already getting headaches? Worry not, because Data Science Dojo’s Apache Zeppelin instance fixes all of that. But before we delve further into it, let’s get to know some basics. 

 

What are Machine Learning Operations?  

Machine Learning is a branch of Artificial Intelligence that deals with models that produce outcomes based on some learned pre-existing data. It provides automation and reduces the workload of users. ML converges with Data Science and Engineering and that gives birth to some necessary operations to be performed to acquire the output of any task.

These operations include ETL (Extraction, Transform, Load) or ELT, drawing interactive visualizations, running queries, training and testing ML models and several other functions. 

Pro Tip: Join our 6-months instructor-led Data Science Bootcamp to master machine learning skills. 

 

Challenges for individuals 

 Wanting to explore and visualize your data but not knowing the methodology of the new tool is not only a red flag but also demands extraneous skills to be learnt to proceed with your job. Or you would have to switch among different environments to achieve your goal which is again – time-consuming, and needless to say time is of the essence for data scientists and engineers when they must deliver a task.

In this scenario, switching from one tool to another which you may know how to use or may not, is time and cost intensive. What if a data driven interactive environment having several interpreters ready to be worked with in one place is provided and you just select your favorite language and break the ice? 

 

ML Operations with Apache Zeppelin 

Apache Zeppelin is an open-source tool that equips you with a web-based notebook that can be used for data processing and querying, handling big data, training and testing models, interactive data analytics, visualization, and exploration. Vibrant designs and pictures generated can save time for users in the identification of key patterns in data and ultimately accelerates the decision-making processes.

It contains different pre-installed interpreters but also allows you to plug in your own various language backends for desirability. Apache Zeppelin supports many data sources which allow you to synthesize your data to visualize into interactive plots and charts. You can also create dynamic forms in your notebook and can share your notebook with collaborators.              

Apache Zeppelin
Apache Zeppelin Data Science Dojo

          

(Picture Courtesy: https://zeppelin.apache.org/ ) 

 

Key features 

  • Zeppelin delivers an optimized and interactive UI that enhances the plots, charts, and other diagrams. You can also create dynamic forms in your notebook along with other markdowns to fancify your note 
  • It’s open-source and allows vendors to make Zeppelin highly customized according to use-case requirements that vary from industry to industry 
  • The choice to select a learned backend from a variety of pre-installed ones or the feasibility to add your own customizable language adds to the user-friendliness, flexibility, and adaptability 
  • It supports Big Data databases like Hive and Spark. It also provides support for web sockets so you can share your web page by echoing the output of the browser and creating live reports 
  • Zeppelin provides an in-build job manager who keeps track of the condition or status of various notebooks 

 

What Data Science Dojo has for you 

Our Zeppelin instance serves as a web-accessible programming environment with miscellaneous pre-installed interpreters. In our service users can switch between different interpreters like processing data with python and then visualizing it by querying with SQL. The pre-installed backends provide the feasibility to perform the task using your accustomed language instead of learning a new tool. 

  • A web-accessible Zeppelin environment 
  • Several pre-installed language-backends/interpreters 
  • Various tutorial notebooks containing codes for understandability 
  • A Job manager responsible for monitoring the status of the notebooks 
  • A Notebook Repos feature to manage your notebook repositories’ settings 
  • Ability to import notes from JSON file or URL 
  • In-build functionality to add and modify your own customized interpreters 
  • Credential management service 

 

Our instance supports the following interpreters: 

  • Alluxio 
  • Angular 
  • Beam 
  • BigQuery 

And many others which you check by taking a quick peek here: Zeppelin on Market Place  

Conclusion 

Efficient resource requirement for processing, visualizing, and training large data was one of the areas of concern when working on traditional desktop environments. The other area of concern includes the burden of working with non-familiar backends or switching among different accustomed environments. With our Zeppelin instance, both concerns are put to rest.

When coupled with Microsoft Azure services and processing speed, it outperforms the traditional counterparts because data-intensive computations aren’t performed locally, but in the cloud. You can collaborate and share notebooks with various stakeholders within and outside the company while monitoring the status of each 

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We are therefore adding a free Zeppelin Notebook Environment dedicated specifically for Machine Learning and Data Science operations on Azure Market Place. Don’t wait to install this offer by Data Science Dojo, your ideal companion in your journey to learn data science! 

Click on the button below to head over to the Azure Marketplace and deploy Apache Zeppelin for FREE by clicking on “Get it now”.

Apache Zeppelin
Note: You’ll have to sign up to Azure, for free, if you do not have an existing account.

September 20, 2022