...
Full Bio
Today's Technology-Data Science
258 days ago
How to build effective machine learning models?
258 days ago
Why Robotic Process Automation Is Good For Your Business?
258 days ago
IoT-Advantages, Disadvantages, and Future
259 days ago
Look Artificial Intelligence from a career perspective
259 days ago
Every Programmer should strive for reading these 5 books
578511 views
Why you should not become a Programmer or not learn Programming Language?
237036 views
See the Salaries if you are willing to get a Job in Programming Languages without a degree?
151803 views
Highest Paid Programming Languages With Highest Market Demand
136650 views
Have a look of some Top Programming Languages used in PubG
132822 views
See some Best-Known Big Data tools, their Advantages and Disadvantages to Analyze your Data
- The core strength of Hadoop is its HDFS (Hadoop Distributed File System) which has the ability to hold all type of data - video, images, JSON, XML, and plain text over the same file system.
- Highly useful for R&D purposes.
- Provides quick access to data.
- Highly scalable
- Highly-available service resting on a cluster of computers
- Sometimes disk space issues can be faced due to its 3x data redundancy.
- I/O operations could have been optimized for better performance.
- Comprehensive distribution
- Cloudera Manager administers the Hadoop cluster very well.
- Easy implementation.
- Less complex administration.
- High security and governance
- Few complicating UI features like charts on the CM service.
- Multiple recommended approaches for installation sounds confusing.
- However, the Licensing price on a per-node basis is pretty expensive.
- No single point of failure.
- Handles massive data very quickly.
- Log-structured storage
- Automated replication
- Linear scalability
- Simple Ring architecture
- Requires some extra efforts in troubleshooting and maintenance.
- Clustering could have been improved.
- The row-level locking feature is not there.
- Simple ETL operations
- Integrates very well with other technologies and languages.
- Rich algorithm set.
- Highly usable and organized workflows.
- Automates a lot of manual work.
- No stability issues.
- Easy to set up.
- Data handling capacity can be improved.
- Occupies almost the entire RAM.
- Could have allowed integration with graph databases.
- Device friendly. Works very well on all type of devices' mobile, tablet or desktop.
- Fully responsive
- Fast
- Interactive
- Brings all the charts in one place.
- Great customization and export options.
- Requires zero codings.
- Easy to learn.
- Provides support for multiple technologies and platforms.
- No hiccups in installation and maintenance.
- Reliable and low cost.
- Limited analytics.
- Slow for certain use cases.
- Scalable
- Secure
- Supported by a dedicated full-time development team.
- Supports the cloud-based environment. Works well with Amazonâ??s AWS.
- The architecture is based on commodity computing clusters which provide high performance.
- Parallel data processing.
- Fast, powerful and highly scalable.
- Supports high-performance online query applications.
- Cost-effective and comprehensive.
- Reliable at scale.
- Very fast and fault tolerant.
- Guarantees the processing of data.
- It has multiple use cases real-time analytics, log processing, ETL (Extract-Transform-Load), continuous computation, distributed RPC, machine learning.
- Difficult to learn and use.
- Difficulties with debugging.
- Use of Native Scheduler and Nimbus become bottlenecks.
- Simple and fun to use.
- Fast and scalable.
- True real-time streaming.
- Write Once Run Anywhere (WORA) architecture.
- Streamlines ETL and ELT for Big data.
- Accomplish the speed and scale of spark.
- Accelerates your move to real-time.
- Handles multiple data sources.
- Provides numerous connectors under one roof, which in turn will allow you to customize the solution as per your need.
- Community support could have been better.
- Could have an improved and easy to use interface
- Difficult to add a custom component to the palette.
- Open source Java core.
- The convenience of front-line data science tools and algorithms.
- The facility of code-optional GUI.
- Integrates well with APIs and cloud.
- Superb customer service and technical support.
- Faster time to value.
- Increased flexibility and scale.
- Optimized spending
- Enhanced adoption of Big data analytics.
- Easy to use.
- Eliminates vendor and technology lock-in.
- Available across all regions of the AWS worldwide.
- Great flexibility to create the type of visualizations you want (as compared with its competitor products).
- Data blending capabilities of this tool are just awesome.
- Offers a bouquet of smart features and is razor sharp in terms of its speed.
- Out of the box support for connection with most of the databases.
- No-code data queries.
- Mobile-ready, interactive and shareable dashboards.
- Formatting controls could be improved.
- Could have a built-in tool for deployment and migration amongst the various tableau servers and environments.
- R's biggest advantage is the vastness of the package ecosystem.
- Unmatched Graphics and charting benefits.