#apache cassandra experts

[sandipanks]

Explores the importance of scalability in the digital world, focusing on Apache Cassandra's unique architecture and cutting-edge features. It delves into the reasons behind Cassandra's rise as a preferred choice for modern data-driven applications, highlighting its remarkable scaling capabilities.

Read this blog to know more -https://www.ksolves.com/blog/big-data/apache-cassandra/apache-cassandra-scaling-guide-why-does-scalability-matter

[xaltius]

Becoming a Full-Stack Data Scientist: Bridging the Gap between Research and Production

For years, the ideal data scientist was often portrayed as a brilliant researcher, adept at statistics, machine learning algorithms, and deep analytical dives within the comfortable confines of a Jupyter notebook. While these core skills remain invaluable, the landscape of data science has dramatically shifted by mid-2025. Companies are no longer content with insightful reports or impressive model prototypes; they demand operationalized AI solutions that deliver tangible business value.

This shift has given rise to the concept of the "Full-Stack Data Scientist" – an individual capable of not just building models, but also taking them from the initial research phase all the way to production, monitoring, and maintenance. This role bridges the historically distinct worlds of data science (research) and software/ML engineering (production), making it one of the most in-demand and impactful positions in the modern data-driven organization.

Why the "Full-Stack" Evolution?

The demand for full-stack data scientists stems from several critical needs:

Accelerated Time-to-Value: The longer a model remains a "research artifact," the less value it generates. Full-stack data scientists streamline the transition from experimentation to deployment, ensuring insights are quickly converted into actionable products or services.

Reduced Silos and Improved Collaboration: When data scientists can speak the language of engineering and understand deployment challenges, collaboration with MLOps and software engineering teams becomes far more efficient. This reduces friction and miscommunication.

End-to-End Ownership & Accountability: A full-stack data scientist can take ownership of a project from inception to ongoing operation, fostering a deeper understanding of its impact and facilitating quicker iterations.

Operational Excellence: Understanding how models behave in a production environment (e.g., data drift, model decay, latency requirements) allows for more robust model design and proactive maintenance.

Cost Efficiency: In smaller teams or startups, a full-stack data scientist can cover multiple roles, optimizing resource allocation.

The Full-Stack Data Scientist's Skillset: Beyond the Notebook

To bridge the gap between research and production, a full-stack data scientist needs a diverse and expanded skillset:

1. Core Data Science Prowess: (The Foundation)

Advanced ML & Deep Learning: Proficiency in various algorithms, model selection, hyperparameter tuning, and understanding the nuances of different model architectures (e.g., Transformers, Diffusion Models for GenAI applications).

Statistics & Mathematics: A solid grasp of statistical inference, probability, linear algebra, and calculus to understand model assumptions and interpret results.

Data Analysis & Visualization: Expert-level exploratory data analysis (EDA), data cleaning, feature engineering, and compelling data storytelling.

Programming Languages: Mastery of Python (with libraries like Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch) and often R, for data manipulation, modeling, and scripting.

2. Data Engineering Fundamentals: (Getting the Data Right)

SQL & Database Management: Expert-level SQL for querying, manipulating, and optimizing data from relational databases. Familiarity with NoSQL databases (e.g., MongoDB, Cassandra) is also valuable.

Data Pipelines (ETL/ELT): Understanding how to build, maintain, and monitor data pipelines using tools like Apache Airflow, Prefect, or Dagster to ensure data quality and timely delivery for models.

Big Data Technologies: Experience with distributed computing frameworks like Apache Spark for processing and transforming large datasets.

Data Warehousing/Lakes: Knowledge of data warehousing concepts and working with data lake solutions (e.g., Databricks, Snowflake, Delta Lake) for scalable data storage.

3. MLOps & Software Engineering: (Bringing Models to Life)

Version Control (Git): Non-negotiable for collaborative code development, model versioning, and reproducibility.

Containerization (Docker): Packaging models and their dependencies into portable, isolated containers for consistent deployment across environments.

Orchestration (Kubernetes): Understanding how to manage and scale containerized applications in production environments.

Cloud Platforms: Proficiency in at least one major cloud provider (AWS, Azure, Google Cloud) for deploying, managing, and scaling ML workloads and data infrastructure. This includes services like SageMaker, Azure ML, Vertex AI.

Model Serving Frameworks: Knowledge of tools like FastAPI, Flask, or TensorFlow Serving/TorchServe to expose models as APIs for inference.

Monitoring & Alerting: Setting up systems (e.g., Prometheus, Grafana, MLflow Tracking, Weights & Biases) to monitor model performance, data drift, concept drift, and system health in production.

CI/CD (Continuous Integration/Continuous Deployment): Automating the process of building, testing, and deploying ML models to ensure rapid and reliable updates.

Basic Software Engineering Principles: Writing clean, modular, testable, and maintainable code; understanding design patterns and software development best practices.

4. Communication & Business Acumen: (Driving Impact)

Problem-Solving: The ability to translate ambiguous business challenges into well-defined data science problems.

Communication & Storytelling: Effectively conveying complex technical findings and model limitations to non-technical stakeholders, influencing business decisions.

Business Domain Knowledge: Understanding the specific industry or business area to build relevant and impactful models.

Product Thinking: Considering the end-user experience and how the AI solution will integrate into existing products or workflows.

How to Become a Full-Stack Data Scientist

The path to full-stack data science is continuous learning and hands-on experience:

Solidify Core Data Science: Ensure your foundational skills in ML, statistics, and Python/R are robust.

Learn SQL Deeply: It's the lingua franca of data.

Dive into Data Engineering: Start by learning to build simple data pipelines and explore distributed processing with Spark.

Embrace MLOps Tools: Get hands-on with Docker, Kubernetes, Git, and MLOps platforms like MLflow. Cloud certifications are a huge plus.

Build End-to-End Projects: Don't just stop at model training. Take a project from raw data to a deployed, monitored API. Use frameworks like Streamlit or Flask to build simple UIs around your models.

Collaborate Actively: Work closely with software engineers, DevOps specialists, and product managers. Learn from their expertise and understand their challenges.

Stay Curious & Adaptable: The field is constantly evolving. Keep learning about new tools, frameworks, and methodologies.

Conclusion

The "Full-Stack Data Scientist" is not just a buzzword; it's the natural evolution of a profession that seeks to deliver real-world impact. While the journey requires a significant commitment to continuous learning and skill expansion, the rewards are immense. By bridging the gap between research and production, data scientists can elevate their influence, accelerate innovation, and truly become architects of intelligent systems that drive tangible value for businesses and society alike. It's a challenging but incredibly exciting time to be a data professional.

[simple-logic]

Why Open Source Database Adoption is Accelerating in 2025

In 2025, open source databases have officially moved from niche solutions to mainstream enterprise choices. With the ever-growing need for scalable, cost-efficient, and flexible data management systems, organizations are increasingly turning toward open source database management solutions to drive innovation and reduce vendor lock-in. From MySQL to PostgreSQL and MongoDB, the popularity of these platforms has surged across industries.

But why are so many businesses making the switch now? Let’s explore the growing adoption of open source databases, their features, benefits, and how to implement them in your IT ecosystem.

Key Features of Open Source Databases

✔️ Source Code Access – Modify and optimize your database to suit your business logic. ✔️ Community-Driven Development – Get regular updates, security patches, and support from global communities. ✔️ Cross-Platform Compatibility – Open source databases are platform-independent and highly portable. ✔️ Support for Advanced Data Models – Handle structured, semi-structured, and unstructured data easily. ✔️ Integrations and Extensions – Access a variety of plug-ins and connectors to expand functionality.

Why Open Source Database Adoption is Booming in 2025

💡 1. Cost-Effectiveness

No hefty licensing fees. Businesses save significantly on software costs while still getting robust features.

⚡ 2. Flexibility and Customization

Developers can fine-tune the database, adjust configurations, or build new functionalities—something proprietary systems limit.

🔒 3. Strong Security and Transparency

With open source, vulnerabilities are exposed and fixed faster thanks to large developer communities.

🚀 4. Scalable for Modern Applications

Whether it's IoT, big data, or AI-driven apps, open source databases scale well horizontally and vertically.

🌐 5. No Vendor Lock-In

Businesses can avoid being stuck with a single provider and switch or scale as needed.

Steps to Successfully Adopt an Open Source Database

✅ Step 1: Evaluate Your Current Infrastructure

Understand your existing data structure, workloads, and performance needs.

✅ Step 2: Choose the Right Database (MySQL, PostgreSQL, MongoDB, etc.)

Pick a database based on your data type, scale, transaction needs, and technical stack.

✅ Step 3: Plan Migration Carefully

Work with experts to move from proprietary systems to open source databases without data loss or downtime.

✅ Step 4: Monitor & Optimize Post-Migration

Use tools for performance monitoring, indexing, and query optimization to get the best out of your new setup.

Real-World Use Cases of Open Source Databases

🏥 Healthcare: Managing patient data securely and affordably. 🛍️ E-commerce: Handling high transaction volumes with scalable databases. 💼 Enterprises: Leveraging PostgreSQL and MySQL for internal applications. 📱 Mobile Apps: Using MongoDB for flexible and fast mobile backend support.

FAQs on Open Source Database Adoption

❓ Are open source databases secure for enterprise use?

Yes, most modern open source databases are highly secure with active communities and regular updates.

❓ What are some top open source databases in 2025?

PostgreSQL, MySQL, MariaDB, MongoDB, and Apache Cassandra are leading the pack.

❓ Will I need in-house expertise to manage it?

Not necessarily. You can always work with managed service providers like Simple Logic to handle everything from setup to optimization.

❓ Can open source databases support enterprise-scale apps?

Absolutely! They support clustering, replication, high availability, and advanced performance tuning.

❓ How can I migrate from my current database to an open source one?

With expert planning, data assessment, and the right tools. Reach out to Simple Logic for a guided and smooth transition.

Conclusion: Future-Proof Your Business with Open Source Databases

Open source databases are no longer a tech experiment—they're the backbone of modern digital infrastructure. From scalability to security, businesses in 2025 are realizing the value of making the shift. If you're still relying on outdated, expensive proprietary databases, it’s time to explore smarter, more agile options.

✅ Ready to Embrace Open Source? Let’s Talk!

Whether you’re planning to adopt PostgreSQL, MySQL, or any other open source database, Simple Logic offers expert guidance, migration support, and performance tuning to ensure a seamless experience.

📞 Call us today at +91 86556 1654 🌐 Visit: www.simplelogic-it.com

👉 Don’t just follow the trend lead with innovation! 🚀

https://simplelogic-it.com/open-source-database-adoption-in-2025/

[srishadigital]

Big Data Analytics Training - Learn Hadoop, Spark

Big Data Analytics Training – Learn Hadoop, Spark & Boost Your Career

Meta Title: Big Data Analytics Training | Learn Hadoop & Spark Online Meta Description: Enroll in Big Data Analytics Training to master Hadoop and Spark. Get hands-on experience, industry certification, and job-ready skills. Start your big data career now!

Introduction: Why Big Data Analytics?

In today’s digital world, data is the new oil. Organizations across the globe are generating vast amounts of data every second. But without proper analysis, this data is meaningless. That’s where Big Data Analytics comes in. By leveraging tools like Hadoop and Apache Spark, businesses can extract powerful insights from large data sets to drive better decisions.

If you want to become a data expert, enrolling in a Big Data Analytics Training course is the first step toward a successful career.

What is Big Data Analytics?

Big Data Analytics refers to the complex process of examining large and varied data sets—known as big data—to uncover hidden patterns, correlations, market trends, and customer preferences. It helps businesses make informed decisions and gain a competitive edge.

Why Learn Hadoop and Spark?

Hadoop: The Backbone of Big Data

Hadoop is an open-source framework that allows distributed processing of large data sets across clusters of computers. It includes:

HDFS (Hadoop Distributed File System) for scalable storage

MapReduce for parallel data processing

Hive, Pig, and Sqoop for data manipulation

Apache Spark: Real-Time Data Engine

Apache Spark is a fast and general-purpose cluster computing system. It performs:

Real-time stream processing

In-memory data computing

Machine learning and graph processing

Together, Hadoop and Spark form the foundation of any robust big data architecture.

What You'll Learn in Big Data Analytics Training

Our expert-designed course covers everything you need to become a certified Big Data professional:

1. Big Data Basics

What is Big Data?

Importance and applications

Hadoop ecosystem overview

2. Hadoop Essentials

Installation and configuration

Working with HDFS and MapReduce

Hive, Pig, Sqoop, and Flume

3. Apache Spark Training

Spark Core and Spark SQL

Spark Streaming

MLlib for machine learning

Integrating Spark with Hadoop

4. Data Processing Tools

Kafka for data ingestion

NoSQL databases (HBase, Cassandra)

Data visualization using tools like Power BI

5. Live Projects & Case Studies

Real-time data analytics projects

End-to-end data pipeline implementation

Domain-specific use cases (finance, healthcare, e-commerce)

Who Should Enroll?

This course is ideal for:

IT professionals and software developers

Data analysts and database administrators

Engineering and computer science students

Anyone aspiring to become a Big Data Engineer

Benefits of Our Big Data Analytics Training

100% hands-on training

Industry-recognized certification

Access to real-time projects

Resume and job interview support

Learn from certified Hadoop and Spark experts

SEO Keywords Targeted

Big Data Analytics Training

Learn Hadoop and Spark

Big Data course online

Hadoop training and certification

Apache Spark training

Big Data online training with certification

Final Thoughts

The demand for Big Data professionals continues to rise as more businesses embrace data-driven strategies. By mastering Hadoop and Spark, you position yourself as a valuable asset in the tech industry. Whether you're looking to switch careers or upskill, Big Data Analytics Training is your pathway to success.

[futurensetechnologies]

Big Data Technologies You’ll Master in IIT Jodhpur’s PG Diploma

In today’s digital-first economy, data is more than just information—it's power. Successful businesses are set apart by their ability to collect, process, and interpret massive datasets. For professionals aspiring to enter this transformative domain, the IIT Jodhpur PG Diploma offers a rigorous, hands-on learning experience focused on mastering cutting-edge big data technologies.

Whether you're already in the tech field or looking to transition, this program equips you with the tools and skills needed to thrive in data-centric roles.

Understanding the Scope of Big Data

Big data is defined not just by volume but also by velocity, variety, and veracity. With businesses generating terabytes of data every day, there's a pressing need for experts who can handle real-time data streams, unstructured information, and massive storage demands. IIT Jodhpur's diploma program dives deep into these complexities, offering a structured pathway to becoming a future-ready data professional.

Also, read this blog: AI Data Analyst: Job Role and Scope

Core Big Data Technologies Covered in the Program

Here’s an overview of the major tools and technologies you’ll gain hands-on experience with during the program:

1. Hadoop Ecosystem

The foundation of big data processing, Hadoop offers distributed storage and computing capabilities. You'll explore tools such as:

HDFS (Hadoop Distributed File System) for scalable storage

MapReduce for parallel data processing

YARN for resource management

2. Apache Spark

Spark is a game-changer in big data analytics, known for its speed and versatility. The course will teach you how to:

Run large-scale data processing jobs

Perform in-memory computation

Use Spark Streaming for real-time analytics

3. NoSQL Databases

Traditional databases fall short when handling unstructured or semi-structured data. You’ll gain hands-on knowledge of:

MongoDB and Cassandra for scalable document and column-based storage

Schema design, querying, and performance optimization

4. Data Warehousing and ETL Tools

Managing the flow of data is crucial. Learn how to:

Use tools like Apache NiFi, Airflow, and Talend

Design effective ETL pipelines

Manage metadata and data lineage

5. Cloud-Based Data Solutions

Big data increasingly lives on the cloud. The program explores:

Cloud platforms like AWS, Azure, and Google Cloud

Services such as Amazon EMR, BigQuery, and Azure Synapse

6. Data Visualization and Reporting

Raw data must be translated into insights. You'll work with:

Tableau, Power BI, and Apache Superset

Custom dashboards for interactive analytics

Real-World Applications and Projects

Learning isn't just about tools—it's about how you apply them. The curriculum emphasizes:

Capstone Projects simulating real-world business challenges

Case Studies from domains like finance, healthcare, and e-commerce

Collaborative work to mirror real tech teams

Industry-Driven Curriculum and Mentorship

The diploma is curated in collaboration with industry experts to ensure relevance and applicability. Students get the opportunity to:

Attend expert-led sessions and webinars

Receive guidance from mentors working in top-tier data roles

Gain exposure to the expectations and workflows of data-driven organizations

Career Pathways After the Program

Graduates from this program can explore roles such as:

Data Engineer

Big Data Analyst

Cloud Data Engineer

ETL Developer

Analytics Consultant

With its robust training and project-based approach, the program serves as a launchpad for aspiring professionals.

Why Choose This Program for Data Engineering?

The Data Engineering course at IIT Jodhpur is tailored to meet the growing demand for skilled professionals in the big data industry. With a perfect blend of theory and practical exposure, students are equipped to take on complex data challenges from day one.

Moreover, this is more than just academic training. It is IIT Jodhpur BS./BSc. in Applied AI and Data Science, designed with a focus on the practical, day-to-day responsibilities you'll encounter in real job roles. You won’t just understand how technologies work—you’ll know how to implement and optimize them in dynamic environments.

Conclusion

In a data-driven world, staying ahead means being fluent in the tools that power tomorrow’s innovation. The IIT Jodhpur Data Engineering program offers the in-depth, real-world training you need to stand out in this competitive field. Whether you're upskilling or starting fresh, this diploma lays the groundwork for a thriving career in data engineering.

Take the next step toward your future with “Futurense”, your trusted partner in building a career shaped by innovation, expertise, and industry readiness.

Source URL: www.lasttrumpnews.com/big-data-technologies-iit-jodhpur-pg-diploma

[hiringjournal]

What to Look for When Hiring Remote Scala Developers

Scala is a popular choice if you as a SaaS business are looking to build scalable, high-performance applications. Regarded for its functional programming potential and seamless integration with Java, Scala is widely implemented in data-intensive applications, distributed systems, and backend development. 

However, to identify and hire skilled remote software developers with Scala proficiency can be challenging. An understanding of the needed key skills and qualifications can help you find the right fit. Operating as a SaaS company makes efficiency and scalability vital, which is why the best Scala developers can ensure smooth operations and future-proof applications. 

Key Skills and Qualities to Look for When Hiring Remote Scala Developers

Strong knowledge of Scala and functional programming

A Scala developer's proficiency with the language is the most crucial consideration when hiring them. Seek applicants with: 

Expertise in Scala's functional programming capabilities, such as higher-order functions and immutability.

Strong knowledge of object-oriented programming (OOP) principles and familiarity with Scala frameworks such as Play, Akka, and Cats.

You might also need to hire backend developers who are adept at integrating Scala with databases and microservices if your project calls for a robust backend architecture.

Experience in distributed systems and big data

Scala is widely used by businesses for large data and distributed computing applications. The ideal developer should be familiar with:

Kafka for real-time data streaming.  

Apache Spark, a top framework for large data analysis.

Proficiency in NoSQL databases, such as MongoDB and Cassandra.

Hiring a Scala developer with big data knowledge guarantees effective processing and analytics for SaaS organizations managing massive data volumes.

Ability to operate in a remote work environment

Hiring remotely is challenging since it poses several obstacles. Therefore, remote developers must be able to: 

Work independently while still communicating with the team. 

Use collaboration technologies like Jira, Slack, and Git for version control.

Maintain productivity while adjusting to distinct time zones.

Employing engineers with excellent communication skills guarantees smooth project management for companies transitioning to a remote workspace.

Knowledge of JVM and Java interoperability

Scala's interoperability with Java is one of its main benefits. Make sure the developer has experience with Java libraries and frameworks and is knowledgeable about JVM internals and performance tuning before employing them. They must be able to work on projects that call for integration between Java and Scala. Businesses switching from Java-based apps to Scala will find this very helpful.

Problem-solving and code optimization skills 

Writing clear, effective, and maintainable code is a must for any competent Scala developer. Seek applicants who can: 

Optimize and debug code according to best practices. 

Refactor current codebases to increase performance.

Possess expertise in continuous integration and test-driven development (TDD).

Conclusion

It takes more than just technical know-how to choose and hire the best Scala developer. Seek out experts who can work remotely, have experience with distributed systems, and have good functional programming abilities. Long-term success will result from hiring developers with the appropriate combination of skills and expertise. Investing in top Scala talent enables SaaS organizations to create high-performing, scalable applications that propel business expansion.

[adzgurupng]

Essential Skills Needed to Become a Data Scientist

Introduction

The demand for data scientists is skyrocketing, making it one of the most sought-after careers in the tech industry. However, succeeding in this field requires a combination of technical, analytical, and business skills. Whether you're an aspiring data scientist or a business looking to hire top talent, understanding the key skills needed is crucial.

In this article, we'll explore the must-have skills for a data scientist and how they contribute to solving real-world business problems.

1. Programming Skills

A strong foundation in programming is essential for data manipulation, analysis, and machine learning implementation. The most popular languages for data science include:

✔ Python – Preferred for its extensive libraries like Pandas, NumPy, Scikit-learn, and TensorFlow. ✔ R – Ideal for statistical computing and data visualization. ✔ SQL – Essential for querying and managing structured databases.

2. Mathematics & Statistics

Data science is built on mathematical models and statistical methods. Key areas include:

✔ Linear Algebra – Used in machine learning algorithms. ✔ Probability & Statistics – Helps in hypothesis testing, A/B testing, and predictive modeling. ✔ Regression Analysis – Essential for making data-driven predictions.

3. Data Wrangling & Preprocessing

Raw data is often messy and unstructured. Data wrangling is the process of cleaning and transforming it into a usable format. A data scientist should be skilled in:

✔ Handling missing values and duplicate records. ✔ Data normalization and transformation. ✔ Feature engineering to improve model performance.

4. Machine Learning & Deep Learning

Machine learning is at the core of predictive analytics. A data scientist should understand:

✔ Supervised Learning – Regression & classification models. ✔ Unsupervised Learning – Clustering, dimensionality reduction. ✔ Deep Learning – Neural networks, NLP, and computer vision using frameworks like TensorFlow & PyTorch.

5. Big Data Technologies

With businesses dealing with large volumes of data, knowledge of big data tools is a plus:

✔ Apache Hadoop & Spark – For distributed data processing. ✔ Kafka – Real-time data streaming. ✔ NoSQL Databases – MongoDB, Cassandra for handling unstructured data.

6. Data Visualization & Storytelling

Data-driven insights must be presented in an understandable way. Visualization tools help in storytelling, making complex data more accessible. Common tools include:

✔ Tableau & Power BI – For interactive dashboards. ✔ Matplotlib & Seaborn – For statistical visualizations. ✔ Google Data Studio – For business intelligence reporting.

7. Business Acumen & Domain Knowledge

A data scientist must understand business problems and align data insights with strategic goals. This includes:

✔ Industry-specific knowledge (Finance, Healthcare, Marketing, etc.). ✔ Understanding KPIs and decision-making processes. ✔ Communicating technical insights to non-technical stakeholders.

8. Soft Skills: Communication & Problem-Solving

Technical skills alone aren’t enough—effective communication and problem-solving skills are key. A data scientist should:

✔ Explain complex models in simple terms. ✔ Collaborate with cross-functional teams. ✔ Think critically to solve business challenges.

How Adzguru Can Help Businesses with Data Science

At Adzguru, we provide expert Data Science as a Service (DSaaS) to help businesses leverage data for growth. Our offerings include:

✔ AI & Machine Learning Solutions – Custom predictive analytics models. ✔ Big Data Integration – Scalable and real-time data processing. ✔ Business Intelligence & Data Visualization – Actionable insights for better decision-making.

Conclusion

Becoming a successful data scientist requires a blend of technical expertise, analytical thinking, and business acumen. By mastering these skills, professionals can unlock exciting career opportunities, and businesses can harness the power of data-driven decision-making.

Want to explore how data science can transform your business? Check out our Data Science Services today!

[learning-code-ficusoft]

Top Data Science Tools in 2025: Python, R, and Beyond

Top Data Science Tools in 2025: 

Python, R, and Beyond As data science continues to evolve, the tools used by professionals have become more advanced and specialized. 

In 2025, Python and R remain dominant, but several other tools are gaining traction for specific tasks.

 Python: 

Python remains the go-to language for data science. 

Its vast ecosystem of libraries like Pandas, NumPy, SciPy, and TensorFlow make it ideal for data manipulation, machine learning, and deep learning. 

Its flexibility and ease of use keep it at the forefront of the field.

 R: 

While Python leads in versatility, R is still preferred in academia and for statistical analysis. 

Libraries like ggplot2, dplyr, and caret make it a top choice for data visualization, statistical computing, and advanced modeling. 

Jupyter Notebooks:

 An essential tool for Python-based data science, Jupyter provides an interactive environment for coding, testing, and visualizing results. 

It supports various programming languages, including Python and R. 

Apache Spark: 

As the volume of data grows, tools like Apache Spark have become indispensable for distributed computing. 

Spark enables fast processing of large datasets, making it essential for big data analytics and real-time processing. 

SQL and NoSQL Databases: 

SQL remains foundational for managing structured data, while NoSQL databases like MongoDB and Cassandra are crucial for handling unstructured or semi-structured data in real-time applications. 

Tableau and Power BI: 

For data visualization and business intelligence, Tableau and Power BI are the go-to platforms. They allow data scientists and analysts to transform raw data into actionable insights with interactive dashboards and reports. 

AutoML Tools: 

In 2025, tools like H2O.ai, DataRobot, and Google AutoML are streamlining machine learning workflows, enabling even non-experts to build predictive models with minimal coding effort. 

Cloud Platforms (AWS, Azure, GCP): 

With the increasing reliance on cloud computing, services like AWS, Azure, and Google Cloud provide scalable environments for data storage, processing, and model deployment. 

MLOps Tools: As data science moves into production, MLOps tools such as Kubeflow, MLflow, and TFX (TensorFlow Extended) help manage the deployment, monitoring, and lifecycle of machine learning models in production environments. 

As data science continues to grow in 2025, these tools are essential for staying at the cutting edge of analytics, machine learning, and AI. The integration of various platforms and the increasing use of AI-driven automation will shape the future of data science.

[jcmarchi]

Karthik Ranganathan, Co-Founder and Co-CEO of Yugabyte – Interview Series

New Post has been published on https://thedigitalinsider.com/karthik-ranganathan-co-founder-and-co-ceo-of-yugabyte-interview-series/

Karthik Ranganathan, Co-Founder and Co-CEO of Yugabyte – Interview Series

Karthik Ranganathan is co-founder and co-CEO of Yugabyte, the company behind YugabyteDB, the open-source, high-performance distributed PostgreSQL database. Karthik is a seasoned data expert and former Facebook engineer who founded Yugabyte alongside two of his Facebook colleagues to revolutionize distributed databases.

What inspired you to co-found Yugabyte, and what gaps in the market did you see that led you to create YugabyteDB?

My co-founders, Kannan Muthukkaruppan, Mikhail Bautin, and I, founded Yugabyte in 2016. As former engineers at Meta (then called Facebook), we helped build popular databases including Apache Cassandra, HBase, and RocksDB – as well as running some of these databases as managed services for internal workloads.

We created YugabyteDB because we saw a gap in the market for cloud-native transactional databases for business-critical applications. We built YugabyteDB to cater to the needs of organizations transitioning from on-premises to cloud-native operations and combined the strengths of non-relational databases with the scalability and resilience of cloud-native architectures. While building Cassandra and HBase at Facebook (which was instrumental in addressing Facebook’s significant scaling needs), we saw the rise of microservices, containerization, high availability, geographic distribution, and Application Programming Interfaces (API). We also recognized the impact that open-source technologies have in advancing the industry.

People often think of the transactional database market as crowded. While this has traditionally been true, today Postgres has become the default API for cloud-native transactional databases. Increasingly, cloud-native databases are choosing to support the Postgres protocol, which has been ingrained into the fabric of YugabyteDB, making it the most Postgres-compatible database on the market. YugabyteDB retains the power and familiarity of PostgreSQL while evolving it to an enterprise-grade distributed database suitable for modern cloud-native applications. YugabyteDB allows enterprises to efficiently build and scale systems using familiar SQL models.

How did your experiences at Facebook influence your vision for the company?

In 2007, I was considering whether to join a small but growing company–Facebook. At the time, the site had about 30 to 40 million users. I thought it might double in size, but I couldn’t have been more wrong! During my over five years at Facebook, the user base grew to 2 billion. What attracted me to the company was its culture of innovation and boldness, encouraging people to “fail fast” to catalyze innovation.

Facebook grew so large that the technical and intellectual challenges I craved were no longer present. For many years I had aspired to start my own company and tackle problems facing the common user–this led me to co-create Yugabyte.

Our mission is to simplify cloud-native applications, focusing on three essential features crucial for modern development:

First, applications must be continuously available, ensuring uptime regardless of backups or failures, especially when running on commodity hardware in the cloud.

Second, the ability to scale on demand is crucial, allowing developers to build and release quickly without the delay of ordering hardware.

Third, with numerous data centers now easily accessible, replicating data across regions becomes vital for reliability and performance.

These three elements empower developers by providing the agility and freedom they need to innovate, without being constrained by infrastructure limitations.

Could you share the journey from Yugabyte’s inception in 2016 to its current status as a leader in distributed SQL databases? What were some key milestones?

At Facebook, I often talked with developers who needed specific features, like secondary indexes on SQL databases or occasional multi-node transactions. Unfortunately, the answer was usually “no,” because existing systems weren’t designed for those requirements.

Today, we are experiencing a shift towards cloud-native transactional applications that need to address scale and availability. Traditional databases simply can’t meet these needs. Modern businesses require relational databases that operate in the cloud and offer the three essential features: high availability, scalability, and geographic distribution, while still supporting SQL capabilities. These are the pillars on which we built YugabyteDB and the database challenges we’re focused on solving.

In February 2016, the founders began developing YugabyteDB, a global-scale distributed SQL database designed for cloud-native transactional applications. In July 2019, we made an unprecedented announcement and released our previously commercial features as open source. This reaffirmed our commitment to open-source principles and officially launched YugabyteDB as a fully open-source relational database management system (RDBMS) under an Apache 2.0 license.

The latest version of YugabyteDB (unveiled in September) features enhanced Postgres compatibility. It includes an Adaptive Cost-Based Optimizer (CBO) that optimizes query plans for large-scale, multi-region applications, and Smart Data Distribution that automatically determines whether to store tables together for lower latency, or to shard and distribute data for greater scalability. These enhancements allow developers to run their PostgreSQL applications on YugabyteDB efficiently and scale without the need for trade-offs or complex migrations.

YugabyteDB is known for its compatibility with PostgreSQL and its Cassandra-inspired API. How does this multi-API approach benefit developers and enterprises?

YugabyteDB’s multi-API approach benefits developers and enterprises by combining the strengths of a high-performance SQL database with the flexibility needed for global, internet-scale applications.

It supports scale-out RDBMS and high-volume Online Transaction Processing (OLTP) workloads, while maintaining low query latency and exceptional resilience. Compatibility with PostgreSQL allows for seamless lift-and-shift modernization of existing Postgres applications, requiring minimal changes.

In the latest version of the distributed database platform, released in September 2024, features like the Adaptive CBO and Smart Data Distribution enhance performance by optimizing query plans and automatically managing data placement. This allows developers to achieve low latency and high scalability without compromise, making YugabyteDB ideal for rapidly growing, cloud-native applications that require reliable data management.

AI is increasingly being integrated into database systems. How is Yugabyte leveraging AI to enhance the performance, scalability, and security of its SQL systems?

We are leveraging AI to enhance our distributed SQL database by addressing performance and migration challenges. Our upcoming Performance Copilot, an enhancement to our Performance Advisor, will simplify troubleshooting by analyzing query patterns, detecting anomalies, and providing real-time recommendations to troubleshoot database performance issues.

We are also integrating AI into YugabyteDB Voyager, our database migration tool that simplifies migrations from PostgreSQL, MySQL, Oracle, and other cloud databases to YugabyteDB. We aim to streamline transitions from legacy systems by automating schema conversion, SQL translation, and data transformation, with proactive compatibility checks. These innovations focus on making YugabyteDB smarter, more efficient, and easier for modern, distributed applications to use.

What are the key advantages of using an open-source SQL system like YugabyteDB in cloud-native applications compared to traditional proprietary databases?

Transparency, flexibility, and robust community support are key advantages when using an open-source SQL system like YugabyteDB in cloud-native applications. When we launched YugabyteDB, we recognized the skepticism surrounding open-source models. We engaged with users, who expressed a strong preference for a fully open database to trust with their critical data.

We initially ran on an open-core model, but rapidly realized it needed to be a completely open solution. Developers increasingly turn to PostgreSQL as a logical Oracle alternative, but PostgreSQL was not built for dynamic cloud platforms. YugabyteDB fills this gap by supporting PostgreSQL’s feature depth for modern cloud infrastructures. By being 100% open source, we remove roadblocks to adoption.

This makes us very attractive to developers building business-critical applications and to operations engineers running them on cloud-native platforms. Our focus is on creating a database that is not only open, but also easy to use and compatible with PostgreSQL, which remains a developer favorite due to its mature feature set and powerful extensions.

The demand for scalable and adaptable SQL solutions is growing. What trends are you observing in the enterprise database market, and how is Yugabyte positioned to meet these demands?

Larger scale in enterprise databases often leads to increased failure rates, especially as organizations deal with expanded footprints and higher data volumes. Key trends shaping the database landscape include the adoption of DBaaS, and a shift back from public cloud to private cloud environments. Additionally, the integration of generative AI brings opportunities and challenges, requiring automation and performance optimization to manage the growing data load.

Organizations are increasingly turning to DBaaS to streamline operations, despite initial concerns about control and security. This approach improves efficiency across various infrastructures, while the focus on private cloud solutions helps businesses reduce costs and enhance scalability for their workloads.

YugabyteDB addresses these evolving demands by combining the strengths of relational databases with the scalability of cloud-native architectures. Features like Smart Data Distribution and an Adaptive CBO, enhance performance and support a large number of database objects. This makes it a competitive choice for running a wide range of applications.

Furthermore, YugabyteDB allows enterprises to migrate their PostgreSQL applications while maintaining similar performance levels, crucial for modern workloads. Our commitment to open-source development encourages community involvement and provides flexibility for customers who want to avoid vendor lock-in.

With the rise of edge computing and IoT, how does YugabyteDB address the challenges posed by these technologies, particularly regarding data distribution and latency?

YugabyteDB’s distributed SQL architecture is designed to meet the challenges posed by the rise of edge computing and IoT by providing a scalable and resilient data layer that can operate seamlessly in both cloud and edge contexts. Its ability to automatically shard and replicate data ensures efficient distribution, enabling quick access and real-time processing. This minimizes latency, allowing applications to respond swiftly to user interactions and data changes.

By offering the flexibility to adapt configurations based on specific application requirements, YugabyteDB ensures that enterprises can effectively manage their data needs as they evolve in an increasingly decentralized landscape.

As Co-CEO, how do you balance the dual roles of leading technological innovation and managing company growth?

Our company aims to simplify cloud-native applications, compelling me to stay on top of technology trends, such as generative AI and context switches. Following innovation demands curiosity, a desire to make an impact, and a commitment to continuous learning.

Balancing technological innovation and company growth is fundamentally about scaling–whether it’s scaling systems or scaling impact. In distributed databases, we focus on building technologies that scale performance, handle massive workloads, and ensure high availability across a global infrastructure. Similarly, scaling Yugabyte means growing our customer base, enhancing community engagement, and expanding our ecosystem–while maintaining operational excellence.

All this requires a disciplined approach to performance and efficiency.

Technically, we optimize query execution, reduce latency, and improve system throughput; organizationally, we streamline processes, scale teams, and enhance cross-functional collaboration. In both cases, success comes from empowering teams with the right tools, insights, and processes to make smart, data-driven decisions.

How do you see the role of distributed SQL databases evolving in the next 5-10 years, particularly in the context of AI and machine learning?

In the next few years, distributed SQL databases will evolve to handle complex data analysis, enabling users to make predictions and detect anomalies with minimal technical expertise. There is an immense amount of database specialization in the context of AI and machine learning, but that is not sustainable. Databases will need to evolve to meet the demands of AI. This is why we’re iterating and enhancing capabilities on top of pgvector, ensuring developers can use Yugabyte for their AI database needs.

Additionally, we can expect an ongoing commitment to open source in AI development. Five years ago, we made YugabyteDB fully open source under the Apache 2.0 license, reinforcing our dedication to an open-source framework and proactively building our open-source community.

Thank you for all of your detailed responses, readers who wish to learn more should visit YugabyteDB.

[aarvyedutech]

How do you learn big data?

My cousin is a big data analyst. I would like to suggest the roadmap that she followed to become an expert in this field and you would not believe that she just did it in 6 months. Here, I will tell you her 6-month strategy which will help you a lot.

But the first and foremost thing I would like to tell you is don't try to learn everything at once. Just focus on the fundamentals and gradually expand your knowledge.

Now, I will tell you the complete roadmap to achieve your goal:

Month 1: Build your foundation

Data Fundamentals:

Understand basic data structures, types, and formats.

Learn about data storage, processing, and analysis.

Programming Basics:

Start learning Python or Java.

Master basic syntax, data types, and control structures.

Database Concepts:

Gain familiarity with relational databases.

Learn SQL for querying and manipulating data.

Month 2: Understand the fundamentals of Big Data

Big Data Concepts:

Dive into volume, velocity, variety, and veracity.

Understand why traditional methods fall short.

Distributed Computing:

Explore Hadoop and Apache Spark.

Learn about parallel processing and fault tolerance.

Big Data Streaming and NoSQL:

Understand real-time data processing.

Explore NoSQL databases like MongoDB and Cassandra.

Month 3: Hands-on Practice & Exploration

Practical Exercises:

Engage in coding challenges and small projects.

Apply programming and database skills to real-world scenarios.

Dataset Exploration:

Work with sample datasets to understand data manipulation.

Experiment with data cleaning, transformation, and analysis.

Month 4: Deepen Your Knowledge

Advanced Topics:

Dive deeper into machine learning algorithms.

Learn optimization techniques for distributed computing. Dev Sharma

[sandipanks]

Apache Cassandra, an open-source distributed NoSQL database, was specifically designed to tackle the challenges of scalability. Let’s explore some of the key features that enable Cassandra to scale effectively in this blog

[synapseindiait]

Big Data Software Development & Consulting Services

At SynapseIndia, our expert team propels businesses toward data-driven success by employing diverse tools such as Apache Hadoop, Spark, MongoDB, Cassandra, Apache Flink, Kafka, and Elasticsearch. We specialize in real-time analytics and advanced data visualization, ensuring your business benefits from the transformative power of Big Data. Know more at https://posts.gle/N3nmPx

[hicglobal12-blog]

DBSync – Cloud Replication Tool for Salesforce

Repeat and Archive your Salesforce information

An easy to understand, cloud replication apparatus, DBSync can consequently imitate the client's Cloud information to their on-premise information stockroom, or on the cloud, for example, AWS RedShift, BigData, and more Cloud.

It consequently makes Salesforce object diagrams, duplicates protests in cluster, or continuously makes or updates Salesforce records from the database.

DBSync is a straightforward, efficient, and financially savvy approach to enables the client to make an information reinforcement productively, safely, and with counteraction for information misfortune to human blunders that aren't found in other Salesforce SLAs.

Key Advantages

·       It empowers you to file your cloud application information with Cloud Replication and satisfy all your consistence with FINRA and other security necessities.

·       Preview based following – Helping you keep steady over administrative prerequisites for forming.

·       Set your mapping plans and overlook it – Zero exertion and organization required as constructions are made and balanced consequently.

·       Anybody can run it – Any business clients without any API experience can download the information legitimately and control it from the database to see the updates directly in the CRM.

·       Run it anyplace – Offers support for the AWS cloud and on-premise database back-ups, for example, Oracle, SQL Server, MySQL, Snowflake, and that's just the beginning.

·       Tackle the intensity of Big Data with Cloud Replication's help for Cassandra and Amazon Redshift.

·       No per-client authorizing, we offer a basic valuing model.

 Focus on its Features

·       Supports Bi-directional synchronization, cloud replication from Salesforce information into your database application, and the other way around.

·       Supports both complete and steady replication via consequently following the last record prepared to repeat just the refreshed information without missing any record.

·       Naturally recreates the Salesforce composition into the database for all chose Salesforce objects without dropping and modifying tables.

·       Supports all Salesforce custom articles, reports, and connections.

·       Works with on-premise information revealing instruments to empower you to run reports from your announcing apparatuses, for example, SQLServer Reporting, Business Object, Cognos, and so on.

·       Send occasion warnings on the overseer's email address to tell for any synchronization occasions.

·       Download Salesforce items, for example, connection, record, substance, and babble feed to a nearby catalog by means of Cloud Replication Console.

·       Accessible for Salesforce Enterprise, Unlimited, Developer, and Professional (API empowered) versions and SQL Server, Oracle, DB2, MySQL, PostgreSQL, Apache Cassandra, and Amazon Redshift databases.

 HIC accomplices with DBSync

Since Salesforce is suspending its information recuperation administration from July 31, 2020, your most loved Salesforce CRM expert HIC Global Solutions joined forces with DBSync to offer you an easy to use, efficient Cloud Replication device to help you reinforcement your information productively, and safely.

We can assist you in setting up your cloud information on the cloud or on-premise in minutes and not days with a good evaluating model that offers a huge amount of highlights at a sensible cost.

Thanks for reading this article and if you like this article and want to read more then please visit HIC Global Solutions | Salesforce implementation partners | https://hicglobalsolutions.com/

[primaverap6guideblog-blog]

Primavera P6 Training - Why Is It Beneficial?

Organizations nowadays have proficient apparatuses for guaranteeing their ideal working. One such application this is perfect for organizers, schedulers, engineers, venture supervisors and for any other individual occupied with arranging, the board and detailing of a task is the Oracle Primavera P6. Numerous enterprises directly from aviation to assembling units are getting profited by this product and here are the advantages venture directors can get when they get Primavera 6 preparing:

They can cut down the danger of expense and timetable invade

They can guarantee viable administration of venture exercises with preparing in this application

They can guarantee ideal usage all things considered

The can get an unmistakable perceivability of what is happening in the venture now and again

It will likewise help in fast and simple estimating

Reports can be effectively gotten thus execution assessment will end up simpler.

It will help in simple separate of task structure and exercises

It will likewise empower simple coordination between every one of the gatherings engaged with a specific undertaking.Click here to read more Primavera P6 Download and Install

Because of the previously mentioned reasons, it is suggested that organizations ought to guarantee that their undertaking supervisors get Primavera P6 preparing. Else, they can likewise contract prepared work force with such an accreditation for this position.

At the point when this is the situation of Oracle Primavera P6, Apache Cassandra can likewise be useful and picking preparing in this application can bring the accompanying advantages:

With the expansion in NoSQL databases, numerous associations are making a move from customary database to open-source model nowadays. With the novel ability to offer continuous execution reports, Cassandra is ending up being the best decision for information investigators, programming specialists and web designers nowadays. It is making extraordinary ponders in the expert existence of these individuals.

After Cassandra Training, yet additionally for normal use, this application can be acquired free of expense as it is an open source stage. This open source highlight has brought forth gigantic network, wherein similarly invested individuals can share information, questions and perspectives. This application can be incorporated with other open-source ventures from Apache too.

As against ace slave design, this stage pursues a distributed engineering thus there is no single purpose of disappointment. As the frameworks are at equivalent level, any number of hubs/servers can be added to Cassandra Cluster.

Like this numerous different subtleties and advantages about this application can be comprehended when the correct Cassandra preparing is acquired.

[ashwinjainraletta-blog]

Data Science

Introduction

Data science has been evolving as one of the most promising and in-demand career paths for skilled professionals. Now-a-days successful data professionals get to know that they must upgrade the traditional skills of analyzing huge amounts of data, data mining, and programming skills. In order to get useful intelligence for their organizations, data scientists must practise the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each and every phase of the process.

The term “data scientist” was coined as recently as 2008 when companies realized the need for data professionals who are skilled in organizing and analyzing massive amounts of data. In a 2009 McKinsey&Company article, Hal Varian, Google's chief economist and UC Berkeley professor of information sciences, business, and economics, predicted the importance of adapting to technology’s influence and reconfiguration of different industries.

Skilled data scientists are capable of identifying appropriate questions, collect data from different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions. These skills are required in all industries, resulting skilled data scientists to be increasingly important to many companies.

Work of a Data Scientist

Data scientists have become important and necessary assets and are present in all organizations. These professionals are well-rounded, data-driven individuals with high-level technical skills who are capable of building complex quantitative algorithms to organize and synthesize large amounts of information used to answer questions and drive strategy in their organization. This is coupled with the experience in communication and leadership needed to deliver tangible results to various stakeholders across an organization or business.

Data scientists need to be creative,innovative,always questioning and result-oriented, with industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical officials. They possess a powerful background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms.

 Scope of becoming Data Scientist

Glassdoor ranked data scientist as the Best Job in America in 2018 for the third year in a row. As increasing amounts of data become more easily available to everyone, large tech companies are now not the only ones in need of data scientists.

The need for data scientists shows no sign of slowing down in the coming years.

Data is everywhere and expansive. A variety of terms related to mining, cleaning, analyzing, and interpreting data are often used interchangeably, but they can actually involve different skill sets and complexity of data.

 Data Scientist

Data scientists examines that which questions need answering and where to get the relevant data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Results are then synthesized and communicated to key stakeholders to drive strategic decision-making in the organization.

Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learning

 Data Analyst

Data analysts bridge the gap between data scientists and business analysts. They are provided the questions that needs to be  answered from any company and then analyze data to find results relevant to the high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.

Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization

Data Engineer

Data engineers manage exponential amounts of rapidly changing data. They focus on the development and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying.

Skills needed: Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)

 Data Science Career Outlook and Salary Opportunities

Data science professionals are paid for their highly technical skill set with competitive salaries and great job opportunities at big and small companies in almost all industries. 

For example, machine learning experts utilize high-level programming skills to create algorithms that continuously gather data and automatically adjust their function to be more effective.

 For more information related to technology do visit:

https://www.technologymoon.com

[berryinfotech]

Big Data Hadoop Training

About Big Data Hadoop Training Certification Training Course

It is an all-inclusive Hadoop Big Data Training Course premeditated by industry specialists considering present industry job necessities to offers exhaustive learning on big data and Hadoop modules. This is an industry recognized Big Data Certification Training course that is known as combination of the training courses in Hadoop developer, Hadoop testing, analytics and Hadoop administrator. This Cloudera Hadoop training will prepare you to clear big data certification.            

Big data Hadoop online training program not only prepare applicant with the important and best concepts of Hadoop, but also give the required work experience in Big Data and Hadoop by execution of actual time business projects.

Big Data Hadoop Live Online Classes are being conducted by using professional grade IT Conferencing System from Citrix.  All the student canintermingle with the faculty in real-time during the class by having chat and voice. There student need to install a light- weight IT application on their device that could be desktop, laptop, mobile and tablet.

So, whether you are planning to start your career, or you need to leap ahead by mastering advanced software, this course covers all things that is expected of expert Big Data professional. Learn skills that will distinguish you instantly from other Big Data Job seekers with exhaustive coverage of Strom, MongoDB, Spark and Cassandra. Quickly join the institution that is well-known worldwide for its course content, hands-on experience, delivery and market- readiness.

Know about the chief points of our Big Data Hadoop Training Online

The Big Data Hadoop Certification course is specially designed to provide you deep knowledge of the Big Data framework by using the Hadoop and Spark, including HDFS, YARN, and MapReduce. You will come to know how to use Pig, Impala to procedure and analyse large datasets stored in the HDFS, and usage Sqoop and Flume for data absorption along with our big Data training.

With our big data course, you will also able to learn the multiple interactive algorithms in Spark and use Spark SQL for creating, transforming and querying data forms. This is guarantee that you will become master real- time data processing by using Spark, including functional programming in Spark, implementing Spark application, using Spark RDD optimization techniques and understanding parallel processing in Spark.

As a part of big data course, you will be needed to produce real- life business- based projects by using CloudLab in the domains of banking, social media, insurance, telecommuting and e-commerce.  This big data Hadoop training course will prepare you for the Cloudera CCA1775 big data certification.

What expertise you will learn with this Big Data Hadoop Training?

Big data Hadoop training will permit you to master the perceptions of the Hadoop framework and its deployment in cluster environment. You would learn to:

Let’s understand the dissimilar components/ features of Hadoop ecosystem such as - HBase, Sqoop, MapReduce, Pig, Hadoop 2.7, Yarn, Hive, Impala, Flume and Apache Spark with this Hadoop course.

·         Be prepared to clear the Big Data Hadoop certification

·         Work with Avro data formats

·         Practice real- life projects by using Hadoop and Apache Spark

·         Facility to make you learn Spark, Spark RDD, Graphx, MLlib writing Spark applications

·         Detailed understanding of Big data analytics

·         Master Hadoop administration activities like cluster,monitoring,managing,troubleshooting and administration

·         Master HDFS, MapReduce, Hive, Pig, Oozie, Sqoop, Flume, Zookeeper, HBase

Setting up Pseudo node and Multi node cluster on Amazon EC2

Master fundamentals of Hadoop 2.7 and YARN and write     applications using them

Configuring ETL tools like Pentaho/Talend to work with     MapReduce, Hive, Pig, etc

Hadoop testing applications using MR Unit and other automation     tools.