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Data Engineering

Erika Balla

In today’s world, technology is evolving at a rapid pace. One of the advanced developments is edge computing. But what exactly is it? And why is it becoming so important? This article will explore edge computing and why it is considered the new frontier in international data science trends.

Understanding edge computing

Edge computing is a method where data processing happens closer to where it is generated rather than relying on a centralized data-processing warehouse. This means faster response times and less strain on network resources.

Some of the main characteristics of edge computing include:

  • Speed: Faster data processing and analysis.
  • Efficiency: Less bandwidth usage, which means lower costs.
  • Reliability: More stable, as it doesn’t depend much on long-distance data transmission.

Benefits of implementing edge computing

Implementing edge computing can bring several benefits, such as:

  • Improved performance: It can be analyzed more quickly by processing data locally.
  • Enhanced security: Data is less vulnerable as it doesn’t travel long distances.
  • Scalability: It’s easier to expand the system as needed.


Read more –> Guide to LLM chatbots: Real-life applications

Data processing at the edge

In data science, edge computing is emerging as a pivotal force, enabling faster data processing directly at the source. This acceleration in data handling allows for realizing real-time insights and analytics previously hampered by latency issues.

Consequently, it requires solid knowledge of the field, either earned through experience or through the best data science course, fostering a more dynamic and responsive approach to data analysis, paving the way for innovations and advancements in various fields that rely heavily on data-driven insights.


Learn practical data science today!


Real-time analytics and insights

Edge computing revolutionizes business operations by facilitating instantaneous data analysis, allowing companies to glean critical insights in real-time. This swift data processing enables businesses to make well-informed decisions promptly, enhancing their agility and responsiveness in a fast-paced market.

Consequently, it empowers organizations to stay ahead, giving opportunities to their employees to learn PG in Data Science, optimize their strategies, and seize opportunities more effectively.

Enhancing data security and privacy

Edge computing enhances data security significantly by processing data closer to its generation point, thereby reducing the distance it needs to traverse.

This localized approach diminishes the opportunities for potential security breaches and data interceptions, ensuring a more secure and reliable data handling process. Consequently, it fosters a safer digital ecosystem where sensitive information is better shielded from unauthorized access and cyber threats.

Adoption rates in various regions

The adoption of edge computing is witnessing a varied pace across different regions globally. Developed nations, with their sophisticated infrastructure and technological advancements, are spearheading this transition, leveraging the benefits of edge computing to foster innovation and efficiency in various sectors.

This disparity in adoption rates underscores the pivotal role of robust infrastructure in harnessing the full potential of this burgeoning technology.

Successful implementations of edge computing

Across the globe, numerous companies are embracing the advantages of edge computing, integrating it into their operational frameworks to enhance efficiency and service delivery.

By processing data closer to the source, these firms can offer more responsive and personalized services to their customers, fostering improved customer satisfaction and potentially driving a competitive edge in their respective markets. This successful adoption showcases the tangible benefits and transformative potential of edge computing in the business landscape.

Government policies and regulations

Governments globally are actively fostering the growth of edge computing by formulating supportive policies and regulations. These initiatives are designed to facilitate the seamless integration of this technology into various sectors, promoting innovation and ensuring security and privacy standards are met.

Through such efforts, governments are catalyzing a conducive environment for the flourishing of edge computing, steering society towards a more connected and efficient future.

Infrastructure challenges

Despite its promising prospects, edge computing has its challenges, particularly concerning infrastructure development. Establishing the requisite infrastructure demands substantial investment in time and resources, posing a significant challenge. The process involves the installation of advanced hardware and the development of compatible software solutions, which can be both costly and time-intensive, potentially slowing the pace of its widespread adoption.

Security concerns

While edge computing brings numerous benefits, it raises security concerns, potentially opening up new avenues for cyber vulnerabilities. Data processing at multiple nodes instead of a centralized location might increase the risk of data breaches and unauthorized access. Therefore, robust security protocols will be paramount as edge computing evolves to safeguard sensitive information and maintain user trust.

Solutions and future directions

A collaborative approach between businesses and governments is emerging to navigate the complexities of implementing edge computing. Together, they craft strategies and policies that foster innovation while addressing potential hurdles such as security concerns and infrastructure development.

This united front is instrumental in shaping a conducive environment for the seamless integration and growth of edge computing in the coming years.

Healthcare sector

In healthcare, computing is becoming a cornerstone for advancing patient care. It facilitates real-time monitoring and swift data analysis, providing timely interventions and personalized treatment plans. This enhances the accuracy and efficacy of healthcare services and potentially saves lives by enabling quicker responses in critical situations.

Manufacturing industry

In the manufacturing sector, it is vital to streamlining and enhancing production lines. By enabling real-time data analysis directly on the factory floor, it assists in fine-tuning processes, minimizing downtime, and predicting maintenance needs before they become critical issues.

Consequently, it fosters a more agile, efficient, and productive manufacturing environment, paving the way for heightened productivity and reduced operational costs.

Smart cities

Smart cities envisioned as the epitome of urban innovation, are increasingly harnessing the power of edge computing to revolutionize their operations. By processing data in affinity to its source, edge computing facilitates real-time responses, enabling cities to manage traffic flows, thereby reducing congestion and commute times.

Furthermore, it aids in deploying advanced sensors that monitor and mitigate pollution levels, ensuring cleaner urban environments. Beyond these, edge computing also streamlines public services, from waste management to energy distribution, ensuring they are more efficient, responsive, and tailored to the dynamic needs of urban populations.

Integration with IoT and 5G

As we venture forward, edge computing is slated to meld seamlessly with burgeoning technologies like the Internet of Things (IoT) and 5G networks. This integration is anticipated to unlock many benefits, including lightning-fast data transmission, enhanced connectivity, and the facilitation of real-time analytics.

Consequently, this amalgamation is expected to catalyze a new era of technological innovation, fostering a more interconnected and efficient world.


Read more –> IoT | New trainings at Data Science Dojo


Role in Artificial Intelligence and Machine Learning


Edge computing stands poised to be a linchpin in the revolution of artificial intelligence (AI) and machine learning (ML). Facilitating faster data processing and analysis at the source will empower these technologies to function more efficiently and effectively. This synergy promises to accelerate advancements in AI and ML, fostering innovations that could reshape industries and redefine modern convenience.

Predictions for the next decade

In the forthcoming decade, the ubiquity of edge computing is set to redefine our interaction with data fundamentally. This technology, by decentralizing data processing and bringing it closer to the source, promises swifter data analysis and enhanced security and efficiency.

As it integrates seamlessly with burgeoning technologies like IoT and 5G, we anticipate a transformative impact on various sectors, including healthcare, manufacturing, and urban development. This shift towards edge computing signifies a monumental leap towards a future where real-time insights and connectivity are not just luxuries but integral components of daily life, facilitating more intelligent living and streamlined operations in numerous facets of society.


Edge computing is shaping up to be a significant player in the international data science trends. As we have seen, it offers many benefits, including faster data processing, improved security, and the potential to revolutionize industries like healthcare, manufacturing, and urban planning. As we look to the future, the prospects for edge computing seem bright, promising a new frontier in the world of technology.

Remember, the world of technology is ever-changing, and staying informed is the key to staying ahead. So, keep exploring data science courses, keep learning, and keep growing!


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Ruhma Khawaja author
Ruhma Khawaja
| July 24

The generation and accumulation of vast amounts of data have become a defining characteristic of our world. This data, often referred to as Big Data, encompasses information from various sources, including social media interactions, online transactions, sensor data, and more.

The sheer volume and variety of Big Data present immense potential for organizations to gain valuable insights, make data-driven decisions, and uncover patterns that were once hidden.

Role of distributed systems in processing massive datasets

As the volume of Big Data continues to grow exponentially, traditional data processing methods have proven insufficient in handling such massive datasets. This is where Distributed Systems step in as a powerful solution. Distributed Systems are a network of interconnected computers that work together to accomplish a common goal. They allow data processing tasks to be distributed across multiple machines, enabling parallel processing and scalability.

Big Data Engineering
Big Data Engineering

Understanding big data engineering

Big data and its characteristics (Volume, Velocity, Variety, Veracity)

Big Data refers to the enormous volume of data that is generated at a high velocity from diverse sources, including structured and unstructured data. Its characteristics can be summarized as follows: 

  • Volume: Big Data involves datasets that are too large to be processed by traditional database management systems. These datasets can range from terabytes to petabytes and beyond. 
  • Velocity: The speed at which Big Data is generated and collected is often rapid. Streaming data from various sources, such as social media or IoT devices, requires real-time processing. 
  • Variety: Big Data comes in various formats, including structured data (e.g., databases), semi-structured data (e.g., XML, JSON), and unstructured data (e.g., text, images, videos). Processing such diverse data types poses a challenge. 
  • Veracity: The reliability and accuracy of Big Data can vary significantly, as it may contain noise, inconsistencies, and errors.


Challenges of traditional data processing methods for handling large datasets

Traditional data processing methods, based on single-server architectures, struggle to cope with the massive scale and complexity of Big Data. Some key challenges include: 

  • Processing Speed: As the data volume and velocity increase, processing times become unacceptably long, leading to delays in insights and decision-making. 
  • Scalability: Traditional systems may not scale effectively to handle the growing data size and user demands. 
  • Data Variety: Single-server setups often struggle to accommodate and process different data types effectively. 
  • Fault Tolerance: With larger datasets, the likelihood of hardware failures or software issues increases, making fault tolerance critical.


Read more –> Big data problem, its impact, and a possible solution for it


Introduction to big data engineering

Big Data Engineering is the discipline that focuses on designing, building, and maintaining systems and solutions to process, store, and analyze massive datasets. It involves various technologies and techniques that enable efficient data processing and retrieval. Distributed Systems play a crucial role in Big Data Engineering by breaking down data processing tasks into smaller sub-tasks, distributing them across multiple machines, and reassembling the results for analysis. 

In the next sections of this blog, we will delve deeper into the technical aspects of Distributed Systems in Big Data Engineering, showcasing code snippets to illustrate how these systems work in practice. Stay tuned for an insightful exploration into the world of Big Data Engineering with Distributed Systems! 

Exploring distributed systems

Distributed Systems, as the name suggests, are a network of interconnected computers that work together to achieve a common goal. In the context of Big Data Engineering, distributed systems play a crucial role in handling the massive scale of data processing and storage. The core principles of distributed systems are: 

  • Decentralization: Distributed Systems do not rely on a single centralized server; instead, they distribute data and processing tasks across multiple nodes. 
  • Scalability: Distributed Systems are designed to scale horizontally by adding more machines to the network, enabling them to handle increasing data loads. 
  • Fault Tolerance: Distributed Systems are built to be resilient to hardware failures or network issues. They use redundancy and replication to ensure data availability. 
  • Consistency: Maintaining data consistency across distributed nodes is a fundamental challenge in these systems. Different algorithms and techniques are employed to achieve eventual consistency.

Key components of distributed systems

  1. Nodes: Nodes are individual machines or servers that form the building blocks of a distributed system. Each node is capable of processing and storing data independently. 
  2. Clusters: Clusters are groups of interconnected nodes that work together to process and store data. Clustering allows for improved performance and fault tolerance as tasks can be distributed across nodes. 
  3. Fault-tolerance: Distributed Systems incorporate fault tolerance mechanisms to ensure data availability even in the face of node failures. Data replication and redundant storage help achieve fault tolerance. 
  4. Distributed File Systems: Distributed Systems often rely on distributed file systems to manage data storage across nodes and ensure efficient data access and retrieval.

Hadoop Distributed File System (HDFS):

HDFS is a distributed file system designed to store vast amounts of data across multiple nodes in a Hadoop cluster. It provides fault tolerance and high throughput for Big Data storage and processing. 

Amazon S3

Amazon Simple Storage Service (S3) is a scalable object storage service provided by Amazon Web Services (AWS). It allows organizations to store and retrieve any amount of data, making it popular for storing and managing Big Data in the cloud. 

Google Cloud Storage

Similar to Amazon S3, Google Cloud Storage provides object storage with high availability, global accessibility, and strong data consistency. It is widely used for Big Data storage and analysis on the Google Cloud Platform.

Replication and data redundancy

One of the key advantages of using distributed data storage is the implementation of data replication and redundancy. Data is replicated across multiple nodes to ensure fault tolerance and high availability. If a node fails, the data can still be accessed from other replicated copies, minimizing the risk of data loss and system downtime.

Real-time data streaming and batch processing for storage optimization

Distributed data storage solutions support both real-time data streaming and batch processing. Real-time streaming allows data to be ingested and processed in real time, enabling organizations to gain insights from data as it is generated. On the other hand, batch processing allows for the processing of large volumes of data at once, optimizing storage and reducing the need for constant data retrieval. 

Incorporating distributed data storage technologies into Big Data Engineering enables organizations to efficiently manage and store massive datasets, ensuring fault tolerance, scalability, and real-time data processing capabilities. The combination of distributed systems and distributed data storage forms the backbone of modern Big Data infrastructure, powering data-driven insights and innovations across various industries.

Distributed data processing: Understanding MapReduce

MapReduce is a programming model and processing paradigm that plays a significant role in distributed data processing. It was popularized by Google and has become a fundamental technique for handling large-scale data operations. The MapReduce model breaks down complex tasks into two main phases: 

  • Map Phase: In this phase, data is divided into smaller chunks and processed in parallel by multiple nodes. Each node applies a “map” function to the data, producing a set of key-value pairs as intermediate outputs. 


  • Reduce Phase: The intermediate outputs from the Map phase are then grouped by their keys and passed to the “reduce” function. The reduced function aggregates and processes the data further, producing the final output.
    The MapReduce model is particularly suitable for data-intensive tasks like data cleaning, transformation, and aggregation. It provides fault tolerance by automatically re-executing failed tasks and is highly scalable due to its parallel processing capabilities. 

Example Python code snippet using MapReduce: 

Apache Spark 

Apache Spark is an open-source distributed computing system that provides an alternative to the MapReduce model. Spark offers a more flexible and memory-resilient approach, allowing for iterative data processing and in-memory caching, which significantly improves performance. 

Spark provides a high-level API in multiple languages like Scala, Python, Java, and SQL, making it accessible to a wide range of developers. It supports various data processing operations, including batch processing, real-time stream processing, machine learning, and graph processing. 

Example Python code snippet using Apache Spark: 

Parallel Processing

In distributed data processing, parallel processing is the key to efficient utilization of resources. By dividing tasks across multiple nodes, it allows for simultaneous data processing, reducing overall execution time. 

Data shuffling is a crucial aspect of distributed processing when data needs to be reorganized and redistributed among nodes. It often occurs during the data exchange between the Map and Reduce phases in MapReduce. While data shuffling enables parallel processing, it can also be a performance bottleneck if not managed efficiently.

To optimize data shuffling, distributed systems use techniques like data partitioning, compression, and data locality. These techniques ensure that data is moved and processed as efficiently as possible, minimizing network overhead and improving overall performance. 

Stream Processing with Distributed Systems

Stream processing is a data processing technique that involves real-time data ingestion, analysis, and action on data as it flows through the system. Unlike traditional batch processing, where data is processed in fixed intervals, stream processing enables organizations to gain insights and respond to events as they happen in real time. 

In Big Data Engineering, stream processing finds numerous applications, including: 

  • Real-time Analytics: Organizations can monitor and analyze data streams to derive immediate insights, enabling faster decision-making and better business outcomes.  
  • Fraud Detection: Stream processing allows the identification of fraudulent activities in real time, helping prevent financial losses and ensuring data security. 
  • Internet of Things (IoT) Data Processing: Stream processing is vital for handling continuous data streams from IoT devices, enabling real-time monitoring and control. 

Apache Flink for stream processing: 


Wrapping up 

In conclusion, stream processing with distributed systems like Apache Kafka, Apache Flink, and Apache Spark Streaming empowers organizations to harness real-time data insights, enabling timely decision-making and enhanced user experiences. By integrating stream processing into their Big Data Engineering pipelines, companies can stay at the forefront of innovation and address evolving customer demands effectively. 

Ruhma Khawaja author
Ruhma Khawaja
| July 6

Data engineering tools are software applications or frameworks specifically designed to facilitate the process of managing, processing, and transforming large volumes of data.

These tools provide data engineers with the necessary capabilities to efficiently extract, transform, and load (ETL) data, build data pipelines, and prepare data for analysis and consumption by other applications.

Data engineering tools offer a range of features and functionalities, including data integration, data transformation, data quality management, workflow orchestration, and data visualization.

data engineering tools

Top 10 data engineering tools to watch out for in 2023

1. Snowflake:

Snowflake is a cloud-based data warehouse platform that provides high scalability, performance, and ease of use. It allows data engineers to store, manage, and analyze large datasets efficiently. Snowflake’s architecture separates storage and compute, enabling elastic scalability and cost-effective operations. It supports various data types and offers advanced features like data sharing and multi-cluster warehouses.

2. Amazon Redshift:

Amazon Redshift is a cloud-based data warehousing service provided by Amazon Web Services (AWS). It is known for its high performance and cost-effectiveness. Amazon Redshift allows data engineers to analyze large datasets quickly using massively parallel processing (MPP) architecture. It integrates seamlessly with other AWS services and supports various data integration and transformation workflows.

3. Google BigQuery:

Google BigQuery is a serverless, cloud-based data warehouse designed for big data analytics. It offers scalable storage and compute resources, enabling data engineers to process large datasets efficiently. BigQuery’s columnar storage and distributed computing capabilities facilitate fast query performance. It integrates well with other Google Cloud services and supports advanced analytics and machine learning features.

4. Apache Hadoop:

Apache Hadoop is an open-source framework for distributed storage and processing of large datasets. It provides a scalable and fault-tolerant ecosystem for big data processing. Hadoop consists of the Hadoop Distributed File System (HDFS) for distributed storage and the MapReduce programming model for parallel data processing. It supports batch processing and is widely used for data-intensive tasks.

5. Apache Spark:

Apache Spark is an open-source, unified analytics engine designed for big data processing. It provides high-speed, in-memory data processing capabilities and supports various programming languages like Scala, Java, Python, and R. Spark offers a rich set of libraries for data processing, machine learning, graph processing, and stream processing. It can handle both batch and real-time data processing tasks efficiently.

6. Airflow:

Apache Airflow is an open-source platform for orchestrating and scheduling data pipelines. It allows data engineers to define and manage complex workflows as directed acyclic graphs (DAGs). Airflow provides a rich set of operators for tasks like data extraction, transformation, and loading (ETL), and it supports dependency management, monitoring, and retries. It offers extensibility and integration with various data engineering tools.

7. dbt (Data Build Tool):

dbt is an open-source data transformation and modeling tool. It allows data engineers to build, test, and maintain data pipelines in a version-controlled manner. dbt focuses on transforming raw data into analytics-ready tables using SQL-based transformations. It enables data engineers to define data models, manage dependencies, and perform automated testing, making it easier to ensure data quality and consistency.

8. Fivetran:

Fivetran is a cloud-based data integration platform that simplifies the process of loading data from various sources into a data warehouse or data lake. It offers pre-built connectors for a wide range of data sources, enabling data engineers to set up data pipelines quickly and easily. Fivetran automates the data extraction, transformation, and loading processes, ensuring reliable and up-to-date data in the target storage.

9. Looker:

Looker is a business intelligence and data visualization platform. It allows data engineers to create interactive dashboards, reports, and visualizations from data stored in data warehouses or other sources. Looker provides a drag-and-drop interface and a flexible modeling layer that enables data engineers to define data relationships and metrics. It supports collaborative analytics and integrates with various data platforms.

10 Tableau:

Tableau is a widely used business intelligence and data visualization tool. It enables data engineers to create interactive and visually appealing dashboards and reports. Tableau connects to various data sources, including data warehouses, spreadsheets, and cloud services. It provides advanced data visualization capabilities, allowing data engineers to explore and analyze data in a user-friendly and intuitive manner. With Tableau, data engineers can drag and drop data elements to create visualizations, apply filters, and add interactivity to enhance data exploration.

Tool Description
Snowflake A cloud-based data warehouse that is known for its scalability, performance, and ease of use.
Amazon Redshift Another popular cloud-based data warehouse. Amazon Redshift is known for its high performance and cost-effectiveness.
Google BigQuery A cloud-based data warehouse that is known for its scalability and flexibility.
Apache Hadoop An open-source framework for distributed storage and processing of large datasets.
Apache Spark An open-source unified analytics engine for large-scale data processing.
Airflow An open-source platform for building and scheduling data pipelines.
dbt (Data Build Tool) An open-source tool for building and maintaining data pipelines.
Fivetran A cloud-based ETL tool that is used to move data from a variety of sources into a data warehouse or data lake.
Looker A business intelligence platform that is used to visualize and analyze data.
Tableau A business intelligence platform that is used to visualize and analyze data.

Benefits of Data Engineering Tools

  • Efficient Data Management: Extract, consolidate, and store large datasets with improved data quality and consistency.
  • Streamlined Data Transformation: Convert raw data into usable formats at scale, automate tasks, and apply business rules.
  • Workflow Orchestration: Schedule and manage data pipelines for smooth flow and automation.
  • Scalability and Performance: Handle large data volumes with optimized processing capabilities.
  • Seamless Data Integration: Connect and integrate data from diverse sources easily.
  • Data Governance and Security: Ensure compliance and protect sensitive data.
  • Collaborative Workflows: Enable team collaboration and maintain organized workflows.


 Wrapping up

In summary, data engineering tools play a crucial role in managing, processing, and transforming data effectively and efficiently. They provide the necessary functionalities and features to handle big data challenges, streamline data engineering workflows, and ensure the availability of high-quality, well-prepared data for analysis and decision-making.

Zaid - Author images
Zaid Ahmed
| May 17

MAANG has become an unignorable buzzword in the tech world. The acronym is derived from “FANG”, representing major tech giants. Initially introduced in 2013, it included Facebook, Amazon, Netflix, and Google. Apple joined in 2017. After Facebook rebranded to Meta in June 2022, the term changed to “MAANG,” encompassing Meta, Amazon, Apple, Netflix, and Google.


Moreover, efficient collaboration and version control are vital for streamlined software development. Enter Git, the ubiquitously distributed version control system that has become the gold standard for managing code repositories. Discover how Git’s best practices enhance productivity, collaboration, and code quality in big organizations.

Top 10 Git practices followed in MAANG

1. Creating a clear and informative repository structure 

To ensure seamless navigation and organization of code repositories, we should follow a well-defined structure for their GitHub repositories. Clear naming conventions, logical folder hierarchies, and README files with essential information are implemented consistently across all projects. This structured approach simplifies code sharing, enhances discoverability, and fosters collaboration among team members. Here’s an example of a well-structured repository:  

Creating a repository structure
Creating a repository structure

By following such a structure, developers can easily locate files and understand the overall project organization.  

2. Utilizing branching strategies for effective collaboration  

The effective utilization of branching strategies has proven instrumental in facilitating collaboration between developers. By following branching models like GitFlow or GitHub Flow, team members can work on separate features or bug fixes without disrupting the main codebase. This enables parallel development, seamless integration, and effortless code reviews, resulting in improved productivity and reduced conflicts. Here’s an example of how branching is implemented: 

Utilizing branching strategies
Utilizing branching strategies

3. Implementing regular code reviews  

MAANG developers place significant emphasis on code quality through regular code reviews. GitHub’s pull request feature is extensively utilized to ensure that each code change undergoes thorough scrutiny. By involving multiple developers in the review process. Code reviews enhance the codebase’s quality and provide valuable learning opportunities for team members. 

Here’s an example of a code review process: 

  1. Developer A creates a pull request (PR) for their code changes. 
  2. Developer B and Developer C review the code, provide feedback, and suggest improvements. 
  3. Developer A addresses the feedback, makes necessary changes, and pushes new commits. 
  4. Once the code meets the quality standards, the PR is approved and merged into the main codebase. 

By following a systematic code review process, MAANG ensures that the codebase maintains a high level of quality and readability.

4. Automated testing and continuous integration 

Automation plays a vital role in MAANG’S GitHub practices, particularly when it comes to testing and continuous integration (CI). MAANG leverages GitHub Actions or other CI tools to automatically build, test, and deploy code changes. This practice ensures that every commit is subjected to a battery of tests, reducing the likelihood of introducing bugs or regressions into the codebase. 

Automated testing and continuous integration
Automated testing and continuous integration

5. Don’t just git commit directly to master 

 Avoid committing directly to the master branch in Git, regardless of whether you follow Gitflow or any other branching model. It is highly recommended to enable branch protection to prevent direct commits and ensure that the code in your main branch is always deployable. Instead of committing directly, it is best practice to manage all commits through pull requests.  

Manage all commits through pull requests
Manage all commits through pull requests

6. Stashing uncommitted changes 

If you’re ever working on a feature and need to do an emergency fix on the project, you could run into a problem. You don’t want to commit to an unfinished feature, and you also don’t want to lose current changes. The solution is to temporarily remove these changes with the Git stash command: 

Stashing uncommitted changes
Stashing uncommitted changes

7. Keep your commits organized 

You just wanted to fix that one feature, but in the meantime got into the flow, took care of a tricky bug, and spotted a very annoying typo. One thing led to another, and suddenly you realized that you’ve been coding for hours without actually committing anything. Now your changes are too vast to squeeze in one commit… 

Keep your commits organized
Keep your commits organized

8. Take me back to good times (when everything works flawlessly!)  

It appears that you’ve encountered a situation where unintended changes were made, resulting in everything being broken. Is there a method to undo these commits and revert to a previous state?  With this handy command, you can get a record of all the commits done in Git. 

Git Command
Git Command

All you must do now is locate the commit before the troublesome one. The notation HEAD@{index} represents the desired commit, so simply replace “index” with the appropriate number and execute the command. 

And there you have it you can revert to a point in your repository where everything was functioning perfectly. Keep in mind to only use this feature locally, as making changes to a shared repository is considered a significant violation.  

9. Let’s confront and address those merge conflicts commits

You are currently facing a complex merge conflict, and despite comparing two conflicting versions, you’re uncertain about determining the correct one. 

Resolving merge conflicts
Resolving merge conflicts

Resolving merge conflicts may not be an enjoyable task, but this command can simplify the process and make your life a bit easier. Often, additional context is needed to determine which branch is the correct one. By default, Git displays marker versions that contain conflicting versions of the two files. However, by choosing the option mentioned, you can also view the base version, which can potentially help you avoid some difficulties. Additionally, you have the option to set it as the default behavior using the provided command.

10. Cherry-Picking commits

Cherry-picking is a Git command, known as git cherry-pick, that enables you to selectively apply individual commits from one branch to another. This approach is useful when you only need certain changes from a specific commit without merging the entire branch. By using cherry-picking, you gain greater flexibility and control over your commit history. 

Cherry-Picking commits
Cherry-Picking commits

In a nutshell

The top 10 Git practices mentioned above are indisputably essential for optimizing development processes, fostering efficient collaboration, and guaranteeing code quality. By adhering to these practices, MAANG’s Git framework provides a clear roadmap to excellence in the realm of technology. 

Prioritizing continuous integration and deployment enables teams to seamlessly integrate changes and promptly deploy new features, resulting in accelerated development cycles and enhanced productivity. Embracing Git’s branching model empowers developers to work on independent features or bug fixes without affecting the main codebase, enabling parallel development and minimizing conflicts. Overall, these Git practices serve as a solid foundation for efficient and effective software development 


Insiyah Talib
| March 15

Data Science Dojo is offering Meltano CLI for FREE on Azure Marketplace preconfigured with Meltano, a platform that provides flexibility and scalability. It comprises four features, it is customizable, observable with a full view of data visualization, testable and versionable to track changes, and can easily be rolled back if needed. 

It is somewhat of a tiring process to install the technology. Then look after the integration and dependency issues. Already feeling tired? It is somehow confusing to resolve the installation errors. Not to worry as Data Science Dojo’s Meltano CLI instance fixes all of that. But before we delve further into it, let us get to know some basics.  

What is Meltano? 

Meltano is an open-source Command Line Interface (CLI) tool that offers a flexible and scalable solution for Extract, Load, and Transform (ELT) processes. It is designed to assist data engineers in transforming, converting, and validating data in a simplified manner while ensuring accuracy and reliability.

The Meltano CLI can efficiently handle complex data engineering tasks, providing a user-friendly interface that simplifies the ELT process. It can also integrate with different data sources, enabling users to extract data from various sources, load it into a target destination, and transform it according to their specific requirements.

In addition, it offers a range of plugins that extend its capabilities and allow users to customize their ELT workflows. These plugins include extractors, loaders, and transformers, among others.

Challenges for individuals

Before Meltano CLI, there were several challenges associated with data integration that made the process difficult and time-consuming. Here are a few of the main challenges: 

  • Lack of Standardization: Data integration tools were often proprietary, which made it difficult to integrate different tools and workflows. This meant that organizations often had to use multiple tools to complete a data integration project. 
  • Complexity: Many data integration tools were complex and required extensive knowledge of programming and data architecture to use effectively. This made it difficult for non-technical users to participate in data integration projects. 
  • Scalability: As data volumes grew, many data integration tools struggled to handle the scale of the data. This led to slow and inefficient data integration processes. 
  • Cost: Many data integration tools were expensive, which made them inaccessible for smaller organizations with limited budgets. 
  • Limited Customization: Many data integration tools offered limited customization options, which made it difficult to adapt the tool to fit the unique needs of an organization.


All in all, it was designed to address many of these challenges by providing an open-source, flexible, and user-friendly tool that can be customized to fit the unique requirements of users.

Meltano CLI for ELT
                                          Meltano CLI for ELT – Data Science Dojo

Why Meltano? 

Meltano CLI stands out as a data engineering tool. It provides flexibility and scalability. It comprises of four features, it is customizable, observable with a full view of data visualization, testable and versionable to track changes, and can easily be rolled back if needed.

Meltano CLI has solved many struggles that make it a compelling choice for many users, including: 

  1. Open-source: It is free and open-source, which means that users can download, use, and modify the source code as per their needs. 
  2. Easy-to-use: It is designed to be easy to use with a simple command-line interface and intuitive user interface. Users can easily configure, execute, and monitor data integration pipelines. 
  3. Customizable: Meltano CLI offers a high degree of customization, allowing users to define custom transformations, connectors, and integrations. 
  4. Modern stack: It is built using modern open-source technologies such as Python, Flask, and Vue.js, making it easy to extend and integrate with other tools. 
  5. GitLab Integration: Meltano CLI is developed by GitLab, which means it can be easily integrated with GitLab for version control, collaboration, and continuous integration and deployment (CI/CD). 

Overall, Meltano CLI is a powerful and flexible data integration tool that offers a unique set of features and benefits that may make it a good choice for certain data integration projects. However, the choice of tool ultimately depends on the specific needs and requirements of the project at hand.


MeltanoHub is the primary location to find all plugins, including Singer taps and targets. It serves as a single source of truth for users, making it easy to discover and use plugins within Meltano. Additionally, users can contribute to the Hub by adding more plugins, which are immediately accessible.

The Hub is maintained by Meltano and the broader community, ensuring that it is continuously curated and up to date. This centralized platform simplifies the process of finding and using plugins, enabling users to enhance their data engineering workflows with ease. 

Key features

Meltano CLI includes several features, including: 

  • Easy to setup and easy to use 
  • Pipeline creation and management 
  • Extract, transform, and load (ETL) processes 
  • Plugin management 
  • Visualization 
  • Configuration management 
  • Version control 
  • Testability 
  • Integration with other tools: It seamlessly integrates with other tools such as dbt, Singer, and Airflow, among others, to enhance your workflow.

What Data Science Dojo has for you?

Azure Virtual Machine is preconfigured with CLI plug-and-play functionality, so you do not have to worry about setting up the environment. 

  • Features include a zero-setup CLI platform that offers a high degree of customization, allowing users to define custom transformations, connectors, and integrations. It is designed to be easy to use with a simple command-line interface and intuitive user interface.
  • Meltano CLI helps you efficiently transform, convert, and validate your data using a simplified process for data engineering, with the assurance of accuracy and reliability. 


And many others which you check by taking a quick peek here: Meltano CLI on Azure Marketplace sets it apart from others is that it is an open-source, flexible, and scalable CLI for ELT+. It is customizable. It is also observable, provides a full view with detailed pipeline logs and statistics, and allows inspection of code for debugging. Meltano is versionable which allows easy tracking and rollback of changes. It is testable and only deploys to production once everything is green. 

Moreover, Meltano CLI is a powerful and flexible data integration tool that offers many benefits over other tools on the market. Its open-source nature, ease of use, integration with other tools, reconfigurability, and community support make it a compelling choice for data integration projects. 


The Meltano CLI comes with pre-configured Ubuntu 20.04 and a ready-to-use project, allowing for a plug-and-play experience without any setup required. By using Azure, the fault tolerance of data pipelines is increased, resulting in higher performance and faster content delivery.

The Meltano CLI provides an open-source, flexible, and scalable CLI for ELT+, allowing for efficient data transformation, conversion, and validation with accuracy and reliability. When combined with Microsoft Azure services, Meltano outperforms traditional methods by performing data-intensive computations in the cloud. Collaboration and sharing of notebooks with stakeholders is also possible.

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 project Environment dedicated specifically to Data Integration and ELT on Azure Market Place. Do not wait to install this offer by Data Science Dojo, your ideal companion in your journey to learn data science! 

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Ali Mohsin - Data engineer
Ali Mohsin
| December 3

Data Science Dojo is offering Apache Airflow for FREE on Azure Marketplace packaged with a pre-configured web environment of Airflow with various data analytics features.  



In this era of tighter data restrictions, it is more important than ever to understand, analyze, and manage your data throughout its lifecycle. It is harder than ever as data volumes rise, and data pipelines get more complicated. A solution is needed Organizations or Individuals must have a complete, scalable, easy-to-analyze platform to manage and monitor the complex workflows and support several integrations. 


What is Apache Airflow?  

Apache Airflow, a powerful open-source tool for authoring, scheduling, and monitoring data and computational workflows. It provides a method that makes it easier to manage, schedule, and coordinate complicated data pipelines from several sources. 


What is DAG? 

A DAG, or Directed Acyclic Graph, in Airflow is a list of all the jobs you wish to execute, arranged to reflect their connections and dependencies. A Python script that expresses the DAG’s structure as code defines a DAG. Researchers’ priori ideas about the connections between and among variables in causal structures are encoded using DAGs. It contains directed edges (arrows), linking nodes (variables), and their paths. Hence A workflow is represented as a DAG, which consists of discrete units of work called Tasks that are ordered considering relationships and data flows. 


Apache Airflow Architecture: 

This powerful and scalable workflow scheduling software is made up of four key parts: 

  • Scheduler: The scheduler keeps track of all DAGs and the jobs they are connected to. To start, it frequently checks the list of open tasks. 
  • Web server: The user interface for Airflow is the web server (The default port Apache Airflow listens to is 8080). It displays the status of the jobs, gives the user access to the databases, and lets them read log files from other remote file stores like Microsoft Azure blobs. 
  • Database: To make sure the schedule retains metadata information, the state of the DAGs and the tasks they are connected to, are saved in the database. The scheduler scans each DAG and records essential data, including schedule intervals, run-by-run statistics, and task instances. 
  • Executors: There are various kinds of executors for different use cases. Few examples of Executors are  SequentialExecutor, LocalExecutor, CeleryExecutor, and KubernetesExecutor 


(With SequentialExecutor, just one task may be carried out at once. No parallel processing is possible. It is useful when testing or debugging. LocalExecutor supports hyperthreading and parallelism. It is excellent for using Airflow on a single node or a local workstation. CeleryExecutor is usually used for managing a distributed Airflow cluster. While using the Kubernetes API, the KubernetesExecutor creates temporary pods for each of the task instances to run in.) 


Key features Apache Airflow provides: 

  • Dynamic Pipelines can be constructed by Airflow dynamic, also as it is constructed in the form of code which gives an edge to dynamic behavior. 
  • Apache Airflow has a rich User Interface that helps the user to manage their workflow easily 
  • It gives a separate code view pallet that enables users to view their DAGs code as well.  
  • Allows users to visualize their DAGs in different forms like Gantt chart, Tree, and Graph. 
  • With ready to use operators in airflow, users can work with various cloud platforms like Microsoft Azure, AWS (Amazon Web Services) etc. 
  • Allows role-based user management to maintain Security and Accessibility.


Apache Airflow with Azure services: 

Apache Airflow leverages the power of Azure services to make the procedure of monitoring and managing complex workflows intuitively. Also with Azure, Airflow made it a more scalable data warehousing platform. Airflow enables users to work in a scalable environment. 



Other open-source Data Engineering solutions put intense competition on Apache Airflow. But it is one of the most robust platforms used by Data Engineers for orchestrating workflows or pipelines. Users can easily visualize your data pipelines’ dependencies, progress, logs, code, trigger tasks, and success status all in a single package.  

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We therefore know the importance of data and the encapsulated insights. Through this offer, we are confident that you can analyze, visualize, and query your data in a collaborative environment with greater ease. 

Install the Apache Airflow 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 Airflow for FREE by clicking on “Try now.”  


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

Saad Shaikh - Associate Data Engineer
Saad Shaikh
| November 11

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. 


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 (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 


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.   


Saad Shaikh - Associate Data Engineer
Saad Shaikh
| November 5

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

Metabase query
Metabase query



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 


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. 

Data Science Dojo
Saad Shaikh
| October 21

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