BIG DATA OPEN SOURCE PROJECTS
Big Data Open Source Projects brings a new hope for budding scholars and students, who wish to stand high in the midst of others. Many scholars and researcher get attracted with this recent technology due to its wide research scope and applicability. We feel renowned for our services, which has benefitted many scholars worldwide.
We always feel proud on our offerings to our students as we fulfill our prime motto i.e student’s satisfaction. Big Data Projects provides best research platform for budding scholars and students with the help of our experts and technocrats. We work closely with our scholars and students to understand their needs, so that we can help the students in the best way. This makes us to stand as world’s no.1 institute for project guidance and support. You can be a part of our research team, if you wish to be.
BIG DATA PROJECTS
Big Data Open Source Projects offers you a platform, where you can flourish and update your knowledge under the guidance of our experts. In this Open source projects can be taken in the areas like scheduling, HDFS, data locality, data skew in Map and Reduce etc. These are few research applications, which came to limelight in recent years. If you feel to explore well in this field, you can have a special session with our experts, who can give you complete guidance for undertaking your project. We have highlighted few most important points below, which scholars must know to get a clear idea for their big data open source projects.
LET’S FIRST UNDERSTAND WHAT BIG DATA MEANS
We here the word “Big data” many times in a day, but most of us really don’t know what it means. Big data is a large collection of data [structured, semi structured and unstructured data], which cannot be computed using traditional technique. The amount of data produced by the mankind is increasing rapidly; big data technology has become an ultimate solution for managing and storing such a huge amount of data. Two major technologies used in big data:
- Operational big data[MongoDB-NoSQL]
- Analytical big data[Massively parallel processing, Map reduce]
There are many software tools suggested to manage big data solutions. Traditional approaches [RDBMS] and many other solutions are not much capable to handle big data issues. Hadoop is one of the most efficient tools used to handle big data issues and data processing due to its parallel processing nature. This is the reason, we have emphasized on Hadoop for big data management.
BIG DATA OPEN SOURCE TOOL- “HADOOP”
Hadoop and its features:
Hadoop is open source software capable of processing in parallel fashion using Map reduce algorithm. It provides software framework to store and process large amount of data efficiently. It is an
- Open source software[available free]
- Provides framework to develop and run software applications
- Massive storage and processing power
Key supports and features:
- Supports Scalable Map and reduce model
- Uses REST based interface for communication which minimizes the number of ports opened in the network.
- Self contained library and support Jar files
- Efficient memory and I/O handling
- Facilitates data co-location and supports Map reduce API
- Support for Apache Hive, Apache Pig , cascading, Apache shark, Apache storm etc
- It can be programmed using C++, Java, PHP, Python, Ruby, Perl, Erlang, Haskell and C#
- To work with Apache Hadoop, we need Java(JDK), SSHD software’s to be installed.
- It works on Windows, Unix, Mac OS X and supports GUI using HUE
- Database access is provided by using NoSQL databases, MongoDB, Cassandra, HBase and Hive.
- Its major benefits include: high computing power, Fault tolerant, Flexible, cost effective and also scalable.
- Major issues in hadoop include lacking tools also for data quality and standardization and Data security.
Core Modules of Hadoop:
- Hadoop common[provides libraries and utilities]
- Hadoop Distributed File system[also Java based system to store data across multiple machines]
- YARN[Yet another resource negotiator-used to schedule and handle resource requests]
- MapReduce[Processing large data in parallel]
Major Algorithms Used:
- K-Means clustering algorithm
- Fuzzy K-Means clustering
- Mean shift Clustering
- Latent Dirichlet allocation
- Singular value Decomposition
- Parallel frequent pattern mining
- Random forest decision tree also based classifier
- Page rank algorithm
- Complementary Naïve Bayes classifier
- 5 Algorithm
Major applications and research area in Hadoop
- Web recommendation system
- Act as data lake[to process ETL and also data quality tasks in parallel using commercial data management technology]
- Used for advanced analytics, query and also reporting in Enterprise data warehouse
- Big data analytics also using hadoop
- Used to store social media, sensor, machine, scientific and transactional data also using low cost hardware’s.
Major Research areas:
- HDFS Namespace expansion
- Modeling of replication policies and also block placement in HDFS
- Working with HOD ports also for campus work queuing systems
- Hadoop security design issues
- Performance enhancement of Map reduce framework
- Shuffle and sort optimization also in MR framework
- Integration of Hadoop tools also with virtualization
- HOD provisioning also for long running services
- Identification of network topology and also diagnosing hardware using hadoop compatible framework
We also have given an overview about Hadoop framework, which is one of the most significant open source software also for big data applications. It’s always better to work upon your own ideas and also interest. If scholars have their own concept or ideas, also approach us, we will support you in the best way we can.