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Apache Hadoop is an assemblage of open-source software utilities that allows you a network of many computers to solve problems related to data and computation. Hadoop developer demand has increased many fold in the past few years. Hadoop gives you a software framework distributed storage and big-data analysis with the help of MapReduce Programming model. The Hadoop structure is such that it handles hardware failures automatically. Hadoop primarily facilitates large-scale storage and processing of big data. All happens within the distributed computing environment. The storage part of the Hadoop Core is called Hadoop Distributed File System, and the processing part is a MapReduce programming model. Hadoop allows faster and more efficient processing of the dataset.
The Apache Hadoop Framework Has The Following Modules:
Hadoop Distributed File System
The Hadoop Framework is mostly written in Java, with some native code in C and command-line utilities composed as shell scripts. To implement the map and reduce parts of the user’s program, you can use any programming language with Hadoop streaming.
Free License: Anyone can download Hadoop by directly going to the Apache Hadoop Website, install it and begin to use it.
Open Source: Hadoop’s source code is openly available. You can modify it to suit your requirements.
Big Data Analytics: It can handle any volume, variety, & value of data. It manages big data with the Ecosystem Approach.
Ecosystem Approach: Hadoop is not limited to only storage & processing. The main feature of Hadoop is that it is an ecosystem. It gets the data from RDBMS, arranges it on the Cluster via HDFS. Then it cleans the data and makes it suitable for analyzing help of Massive Parallel Processing. Lastly, it Analyzes the data & then it Visualizes it.
Shared Nothing Architecture: This implies that Hadoop is a cluster with independent machines. Every node performs its job by utilizing its resources.
Distributed File System: Hadoop, distributes data on various machines in a cluster. It has a built-in capability to stripe & mirror data without using any third-party tools.
Commodity Hardware: Hadoop can run on commodity hardware. It does not require a very high-end server with substantial memory and processing power. Hadoop runs on JBOD (just bunch of disk).
Horizontal Scalability: You do not need to create large clusters. You need to keep on adding nodes as the volume of data increases.
Distributors: Due to the availability of distributors, you can get the bundles, also built-in packages. You do not need to install any individual package.
Android Package Kit or API is the file format for the Android OS. It is used for the distribution and installation of all Android applications.
1. Scalability: Hadoop is a highly scalable storage platform. It can store and distribute massive data sets across numerous servers that operate parallelly.
2. Cost-effective: Hadoop offers a cost-effective storage solution for businesses facing an explosion of data sets. Managing huge volumes of data is not only challenging but also often expensive from a technological viewpoint. Apache Hadoop is adept at bringing data processing and analysis to raw storage. It is a cost-effective replacement of a conventional extract.
3. Flexible: Businesses can easily access new data sources with Hadoop. They can tap into structured and unstructured data to generate value from that data. Companies derive valuable business insights from data sources via Hadoop.
4. Rapid: The base of Hadoop’s storage is a distributed file system that basically ‘maps’ data wherever located. We often find tools for data processing on the same servers where we can identify the data. It results in much faster data processing.
5. Resilient to Failure: When you transfer data to an individual node, it copies that data to other nodes in the cluster. Hence, in case of failure, there is always another copy available for use.
Hadoop Distributed File Systems: Hadoop Application uses a primary storage solution that is the HDFS. A developer can access the application data with this distributed file-system.
Hadoop MapReduce: It is the software framework that enables parallel processing of data.
Hadoop YARN: YARN falls under the resource management technology of Hadoop. It undertakes the task of job-scheduling and resource management.
Hadoop Common: This is a component of the Hadoop ecosystem that has Java libraries and utilities that support Hadoop Modules.
A Hadoop developer is a programmer involved in the building of Big Data applications. He has sufficient knowledge about the components of the Hadoop framework.
Their responsibilities include the design and development of Hadoop system with competent documentation skills. Following are the duties of a Hadoop Developer
Documentation, design, development, and architecture of Hadoop applications.
Installation, configuration, and support of Hadoop.
Writing MapReduce coding for Hadoop clusters.
Building new Hadoop Clusters.
Conversion of sophisticated techniques as well as functional requirements into the designs.
Designing web applications for querying data.
Hassle-free data-tracking at a higher speed.
Making proposals for best practices and standards.
Testing of software prototypes.
Communicating results with the operational team.
Pre-processing of data via Pig and Hive.
Maintaining data security and privacy.
Management and deployment of HBase.
Analysis of large data stores and derive insights.
Close familiarity with Hadoop ecosystem and its components.
Writing reliable, manageable, and high-performance code.
Knowledge of Hadoop, Hive, HBase, and Pig.
Work experience in HQL.
Writing Pig Latin Scripts and MapReduce jobs.
Knowledge of the concepts of multi-threading.
Analytical and problem-solving skills.
Implementing such skills in the Big Data domain.
Knowledge of data loading tools such as Flume, Sqoop etc.
Competent experience in database principles, practices, structures, and theories.
Communication with schedulers.
|ENTERTAINMENT||TRANSPORTATION||NATURAL RESOURCES||LIFE SCIENCES|
If a Big Data professional is looking for a profitable career, Hadoop Developer is the best choice. All businesses, nowadays, are actively focusing on developing applications that help them get insights from their large datasets. It opens great opportunities for Big Data Hadoop professionals.
Hadoop is the open-source distributed computing platform that compresses large stores of data. Big Data is the quantitative-qualitative procedure of collecting knowledge from the vast bulk of data. Hadoop is the tool being used to construct Big Data. Both these technologies are witnessing a fast development. Both have become equally instrumental in quickly developing innovations. While we have been seeing Big Data around for quite an extended period, organizations still seem to be confused about how to unleash their core potential.
Very recently, Cloudera and Hortonworks, the two pillars of the big data Hadoop time, reported the news of their collaboration. These two historic pioneers have helped business organizations to take up projects that were not previously quite possible. Businesses have rapidly begun to understand the capability of these new technologies to convey new data-driven services to their clients. They made it possible for companies to analyze every data they were collecting and utilize that data to make smart and practical decisions.
This new development has made every enterprise open doors to an unparalleled amount and quality of data than any time before. They have better alternatives to come up with services utilizing this data to the fullest. With real-time data, organizations can provide intelligent services and applications which create customer value.
Usage of data, machine learning algorithms are enabling companies to offer new services like hyper-customized retail experiences. Or for banks to predict when some prospective client may be interested in taking a home loan etc.
Regardless of all the developments surrounding Hadoop, Hadoop remains in the centre for several endeavours. Together, Cloudera and Hortonworks will offer clients a better arrangement of services and offerings. It could be an end-to-end cloud significant data offering and the like. The technology world keeps on racing, and Hadoop and Big Data will eventually contain a large number of technologies all coordinated.
|BASIS OF DIFFERENTIATION||RELATIONAL DATABASE||HADOOP|
|Data Types||RDBMS depends on structural data, and one can always identify the schema of the data.||Be it Structured, unstructured or semi-structured- Hadoop stores all kinds of data.|
|Processing||Relational Database provides zero processing capabilities.||Hadoop processes all the data distributed across the cluster.|
|Schema on Read vs Write||Relational Database relies on ‘schema on write'. Hence, schema-validation occurs before the data-loading process.||The ‘schema on read' policy is the base for Hadoop.|
|Reading/Writing Speed||RDMS has a fast reading-process because you already know the schema of the data.||Hadoop has a fast writing-process because it does not validate the schema of data while the writing is going on.|
|Cost||Since it is licensed software, the user has to pay for the software.||Since Hadoop is an open-source framework, the user does not have to pay for it.|
|Use Case||Relational Database facilitates Online Transaction Processing System.||Hadoop facilitates Data Discovery, data analytics or OLAP System.|
Expertise in Hadoop.
Efficient solving of your analytical challenges, including the complex Big Data issues.
Access to quick analytical solutions to your business with no flaws.
Unravelling the complexities of Hadoop clusters by developing new ones.
Building scalable and flexible solutions for your business at an affordable price.
|HADOOP DEVELOPER||HADOOP ADMINISTRATOR||HADOOP ARCHITECT|
|HADOOP ENGINEER||DATA SCIENTIST||HADOOP TESTER|
Job postings and recruiters are always looking for candidates with Hadoop certification.
Hadoop CertificatiHaon gives an edge over the other job roles in the same field, in term of pay-package.
During IJPs, Hadoop Certification accelerates your career.
Helpful for People are trying to transition into Hadoop from different technical backgrounds.
The certification authenticates your hands-on experience in dealing with Big Data.
Verification of your awareness about the latest features of Hadoop.
The accreditation helps speak more confidently about this technology.
It is essential to make sure that you reap maximum benefits from it and that the curriculum includes the latest topics in Apache Hadoop. For instance, by the end of the course, you should have mastered the following concepts in Apache Hadoop.
Master the concepts of Hadoop Distributed File System and MapReduce framework.
Understand data loading techniques using Sqoop and Flume.
Learn to write complex MapReduce programs.
Perform data analytics using Pig and Hive.
Have a good understanding of ZooKeeper service.
Implement best practices for Hadoop Development and Debugging.
Setting up a Hadoop cluster.
Program in MapReduce – both MRv1 and MRv2
Application in YARN (MRv2)
Implement HBase, MapReduce Integration, Advanced Usage and Advanced Indexing.
New features in Hadoop 2.0 – YARN, HDFS Federation, NameNode High Availability.
Implement a Hadoop Project.
Big data is a collection of large and complex data sets, that makes processing using relational database management tools very difficult. Capturing, curating, storing, searching, sharing, transferring, analyzing, and visualizing Big Data is an arduous task. Big Data is an opportunity for companies to derive value from their data successfully. Businesses can now have an essential advantage over their competitors via enhanced business decisions making capabilities.
(It will be great if you can talk about the 5Vs in such questions, whether explicitly asked or not.)
Volume: It is the amount of data growing at an exponential rate in Petabytes and Exabytes.
Velocity: Velocity is the rate at which data is growing, which is very fast. In the present day, yesterday’s information is old data. Social media majorly contributes to the velocity of growing data.
Variety: Variety is the heterogeneity of data types. The data gathered has a variety of formats like videos, audios, CSV, etc. and these various formats represent the variety of data.
Veracity: Veracity is the data in doubt available due to data inconsistency. Data available can sometimes get unorganized and may be challenging to trust. With so many forms of big data, controlling quality and accuracy is very difficult. Mostly, the volume responsible for the lack of quality and efficiency in the data.
Value: Big data is useless unless we can turn it into a profit. We need to ask ourselves- Is the data adding to the benefits of the organization? Is it leading to achievement of higher ROI? Until and unless Big Data analysis adds to the profits of the organization, it is useless.
What is Hadoop? Name its components.
When Big Data came to become a problem, Apache Hadoop became the solution to it. The Apache Hadoop framework provides various services or tools to store and process Big Data. It analyzes Big Data and helps derive business decisions from it.
(We will advise you also to explain the main components of Hadoop.)
What are HDFS and YARN?
HDFS stands for Hadoop Distributed File System. It is the storage unit of Hadoop, responsible for storing data as blocks in a distributed environment. It follows the principles of master and slave topology.
(We recommended that you explain the HDFS components as well)
NameNode: It is the master node in the distributed environment and maintains the metadata information for the blocks of data stored in HDFS.
DataNode: DataNodes are the slave nodes, responsible for data storage in the HDFS.
YARN stands for Yet Another Resource Negotiator. It is the processing framework in Hadoop handling resources. YARN provides an environment where you can execute the processes.
(Just like HDFS, we advise you to explain the two components of YARN as well)
ResourceManager: It receives the processing requests, and passes them to corresponding NodeManagers accordingly. Here, it allocates resources to applications as and when the requirements arise.
NodeManager: NodeManager is on every DataNode, and it is responsible for executing the process on every individual DataNode.
What do you know about the various Hadoop daemons and their roles in a Hadoop cluster?
Ans: First explain the HDFS daemons, i.e. NameNode, DataNode and Secondary NameNode, and then proceed to the YARN daemons, i.e. ResorceManager and NodeManager. End with telling the JobHistoryServer.
NameNode: It is the master node that stores the metadata of all the files. It has information about blocks and the location of the blocks in the cluster.
Datanode: It is the slave node that contains the actual data.
Secondary NameNode: It merges the changes with the Filesystem Image, present in the NameNode. It stores the modified FsImage into resolute storage, which can be useful if NameNode fails.
ResourceManager: It manages resources and schedules applications running on top of YARN.
NodeManager: It runs on slave machines and launches the application’s containers, monitoring their resource usage and reports these to the ResourceManager.
JobHistoryServer: It maintains information about MapReduce jobs after the Application Master closes down.
Compare HDFS with Network Attached Storage (NAS).
First, you need to describe NAS and HDFS, and then compare their features:
NAS stands for Network-attached storage. It is a file-level computer data storage server connected to a computer network that provides data access to a varied set of clients. NAS can either be a hardware or software providing services for storing and accessing files.
HDFS stands for Hadoop Distributed File System. It is a distributed file-system to store data via commodity hardware.
In HDFS Data Blocks are distributed across all the machines within a composed cluster. Dedicated hardware stores NAS data.
HDFS works with MapReduce, where it moves computation to the data. NAS is not for MapReduce since data separate from the estimates.
HDFS uses commodity hardware which saves cost as it comprises of high-end storage devices which is costly.
What do you know about active and passive “NameNodes”?
In High Availability architecture, there are two NameNodes – Active “NameNode” and Passive “NameNode”.
Active “NameNode” works and runs in the cluster.
Passive “NameNode” is a standby component that has data similar to the data of active “NameNode”.
When the active “NameNode” faces a failure, it is replaced by the passive “NameNode” in the cluster. The cluster is, therefore, never without a “NameNode” and hance,t never fails.
Why does one remove or add nodes in a Hadoop cluster frequently?
One of the best features of the Hadoop framework is its utilization of commodity hardware. But this also leads to frequent “DataNode” crashes in a Hadoop cluster. Another feature of the Hadoop Framework is the ease of scale in terms of the rapid growth in data volume. Due to these two reasons, the most common task of a Hadoop administrator is to commission and decommission “Data Nodes” in a Hadoop Cluster.
What happens when two clients try to access the same file in the HDFS?
HDFS allows only exclusive writes. When the first client contacts the NameNode to open the file for writing, the “NameNode” allows the client to create this file through a lease. When the second client tries to open the same data, the NameNode will identify that the contract for the data is with another client. It will not allow him to open the request.
How does NameNode handle DataNode failures?
NameNode receives a Heartbeat (signal) from each of the DataNode in the cluster from time to time. From this, you can understand that DataNode is working fine. When a DataNode fails to send a heartbeat message, it is marked dead after a specific period. The NameNode duplicates the blocks of dead node to another DataNode using the replicas created earlier.
What will you do when NameNode is down?
The NameNode recovery process goes as follows:
Use the file system metadata to start a new NameNode.
Configure the DataNodes and clients so that they can identify this newly started NameNode.
Now the new NameNode will begin to serve the client after complete loading of the last checkpoint FsImage.
On large Hadoop clusters, the NameNode recovery process may consume a lot of time, and this becomes a bigger problem in the case of routine maintenance.
What is a checkpoint?
Checkpointing is a process that takes a FsImage, edit log and composes them into a new FsImage. Instead of redoing an edit log, the NameNode processes the final in-memory state from the FsImage. It decreases the NameNode startup time. Secondary NameNode does checkpointing.
How is HDFS fault-tolerant?
When data is kept accumulated over HDFS, NameNode duplicates the data to several DataNode. The default replication factor is 3. You can change the configuration, according to your requirements if a DataNode dysfunctions, the NameNode itself will copy the data to another node from the replicas and facilitate data-availability. It gives fault tolerance in HDFS.
Can NameNode and DataNode be commodity hardware? Justify.
DataNodes are commodity hardware as it stores data and a large number of it is a requirement. NameNode is the master node, and it stores metadata in HDFS. It requires a huge memory space, so NameNode has to be a high-end machine with sufficient memory space.
Why do we use HDFS for applications having large data sets and not when there are several small files?
HDFS is better for higher amounts of data sets in a single file as compared to the lesser amount of data distributed across multiple files. The NameNode stores the metadata information related to the file system in the RAM. Hence, the amount of memory produces a limit to the number of files in the HDFS file system. Too many data will generate an excess amount of metadata and, storing these metadata in the RAM will pose quite a challenge.
How do you define “block” in HDFS? What is the default block size in Hadoop 1 and Hadoop 2? Can it be changed?
Blocks are the smallest continuous location on your hard drive where you can store data. HDFS stores each in the form of blocks, and distributes it across the Hadoop cluster. Files in HDFS are fragmented into block-sized chunks and stored as independent units.
Hadoop 1 default block size: 64 MB
Hadoop 2 default block size: 128 MB
Yes, blocks can be changed and configured. You can use the dfs block-size parameter in the hdfs-site.xml file to set the size of a block in a Hadoop environment
What is the function ‘jps’ command?
The ‘jps’ command allows you to check if the Hadoop daemons are functioning. It shows all the Hadoop daemons like namenode, datanode, resourcemanager, nodemanager etc. that are functioning on the machine
How do you define “Rack Awareness” in Hadoop?
Rack Awareness is the algorithm in which the “NameNode” chooses how blocks and position their replicas will. The decision is based on rack definitions to reduce network traffic between DataNodes within the same rack. This rule is Replica Placement Policy.
What do you understand by “speculative execution” in Hadoop?
If a node is executing a task slower, the master node can execute another instance of the same job on another node. Then, the responsibility completed first is accepted, and it kills the other one. This process is called “speculative execution”.
How can I restart “NameNode” or all the daemons in Hadoop?
We can restart NameNode via the following methods:
You can stop the NameNode individually using. /sbin /Hadoop-daemon.sh stop namenode command and then start the NameNode using. /sbin/Hadoop-daemon.sh start namenode command.
To stop and start all the daemons, you can use. /sbin/stop-all.sh and then use ./sbin/start-all.sh command which will stop all the daemons first and then start all the daemons.
Differentiate between an “HDFS Block” and an “Input Split.”
The “HDFS Block” is the physical division of the data while “Input Split” is the logical division of the data. HDFS separates data in blocks for keeping the blocks in unison. For processing, MapReduce distributes the data into the input split and delegates it to mapper function.
Name the three modes in which Hadoop can run.
Hadoop can run in the following three modes:
Standalone (local) Method: This is the default mode if you do not make any configurations. In this mode, all the components of Hadoop run as a single Java process using the local file-system.
Pseudo-Distributed Mode: A single-node Hadoop deployment is a running Hadoop system in pseudo-distributed way. In this mode, all the Hadoop services, are executed on a single compute node.
Fully Distributed Mode: If the Hadoop master and slave services in a Hadoop deployment run on separate nodes, they are known to be a fully distributed mode.
What is “MapReduce”? What is the syntax to run a “MapReduce” program?
MapReduce is a framework or a programming model that you use for processing large data sets over a cluster of computers via parallel programming. The syntax for running a MapReduce program is hadoop_jar_file.jar /input_path /output_path.
Give me the main configuration parameters in a “MapReduce” program.
The main configuration parameters in the “MapReduce” framework are:
Job’s input locations in the distributed file system
Job’s output location in the distributed file system
Input format of data
Output format of data
Class with the map function
Type with the reduce function
JAR file including the mapper, reducer and driver classes
Why we can’t perform “aggregation” (addition) in mapper? Why do we need the “reducer” for this?
We cannot perform aggregation in mapper because sorting is not executable in the mapper function. Sorting is possible only on the reducer side, and without sorting, collection is not possible.
During aggregation, we need the output of all the mapper functions. You may not be able to collect this in the map phase, because mappers may be running on a completely separate machine where the data blocks are stored.
If we try to aggregate data at mapper, it demands communication between all mapper functions running on all the different machines. Hence, it will consume high network bandwidth and can result in network bottlenecks.
What is the use of “RecordReader” in Hadoop?
The InputSplit defines a slice of work but does not set the method to access it. The RecordReader class processes the data from its source and changes it into pairs that are suitable for reading by the Mapper task. The “Input Format describes the RecordReader instance”.
Describe “Distributed Cache” in a “MapReduce Framework”.
Distributed Cache is a facility that the MapReduce framework provides to cache files required by applications. Once you have cached data, it will be available on every individual data nodes where your map/reduce tasks are currently running. Now you can access the cache file as a local file in your Mapper or Reducer job.
How do “reducers” communicate with each other?
The MapReduce programming model does not allow reducers to communicate because Reducers run in isolation.
What is the function of a “MapReduce Partitioner”?
A MapReduce Partitioner sends all the values of a single key to the same reducer, thus ensuring proportionate distribution of the map output over the reducers. It redirects the mapper output to the reducer by identifying which reducer is accountable for the particular key.
How to write a custom partitioner?
Custom partitioner for a Hadoop job can be written by following the below steps:
Create a new class that expands Partitioner Class
Override method – get a partition, in the wrapper that runs in the MapReduce.
Add the custom partitioner to the job with method set Partitioner. Or add the custom partitioner to the post as a configured file.
What is a “Combiner”?
A Combiner is a mini reducer that carries the local reduce task. It accepts the input from the mapper on a particular node and transfers the output to the reducer. Combiners enhance the efficiency of MapReduce by reducing the quantum of data that they to the reducers.
Tell me what you know about “SequenceFileInputFormat.”
SequenceFileInputFormat is an input compressed binary file format for reading sequence files. It is exchanging the data between the outputs of one MapReduce job to the information of another MapReduce job. Sequence files are an efficient representation for data that is transferring from one MapReduce job to another.
State the benefits of Apache Pig over MapReduce.
Apache Pig is a platform that analyzes large data sets and provides an abstraction over MapReduce. It reduces the difficulties of writing a MapReduce program.
Pig Latin is a high-level data flow language.
Programmers can achieve the same implementations quickly via Pig Latin, as they would by writing complex Java implementations in MapReduce.
Apache Pig reduces the length of the code by a scale of 20 times which reduces the development period by around 16 times.
Pig has several built-in operators to support data operations like joins, filters, ordering, sorting etc. it is a very hectic job in MapReduce.
Performing a Join operation in Apache Pig is more uncomplicated and more comfortable than it is in MapReduce. You need to execute multiple tasks, one by one, to fulfil the job.
Apache pig also provides nested data types like tuples, bags, and maps that you cannot find in MapReduce.
Name the different data types in Pig Latin.
Pig Latin can manage both atomic data types and complex data types
Nuclear data types are the basic data types used in all the languages like string, int, float, long, double, char, byte, and complex Data Types include Tuple, Map and Bag.
What are the different relational operations in “Pig Latin” with which you have worked?
The Different relational operators in Pig Latin are:
What is a UDF?
When you see that some functions are not there in built-in operators, you can programmatically create User-Defined Functions (UDF). It is to bring those functionalities using languages like Java, Python, Ruby, etc. thereby embedding it in the Script file.
What is “SerDe” in “Hive”?
Apache Hive is a data warehouse system built on top of Hadoop. Its job is to analyze structured and semi-structured data developed by Facebook. Hive removes the difficulties of Hadoop MapReduce.
The SerDe interface allows you to direct Hive about how a record needs to be processed. SerDe is a fusion of a Serializer and a Deserializer. Hive uses SerDe to read and write the table’s row.
Can multiple users use the default “Hive Metastore” at the same time?
Derby database is the default “Hive Metastore”. Multiple processes cannot access it at the same time. It mainly facilitates perform unit tests.
What is the default location where “Hive” stores table data?
Hive stores table data inside HDFS in /user/hive/warehouse by default.
What is Apache HBase?
HBase is an open-source, multifaceted, distributed, adaptable and a NoSQL database written in Java. HBase runs on top of HDFS and gives BigTable like capabilities to Hadoop. It smoothly stores the large data sets. HBase provides faster Read/Write Access on massive datasets and hence, achieves high throughput and low latency.
What are the components of Apache HBase?
HBase has three significant components of HBase are:
Region Server is a component through which they deliver a group of regions to the clients.
HMaster synchronizes and manages the Region Server.
ZooKeeper is a coordinator inside HBase distributed environment. It facilitates server condition maintenance inside the cluster by interfacing via sessions.
What are the components of Region Server?
The components of Region Server include:
Write-Ahead Log (WAL) is a file with every Region Server inside the distributed environment. The WAL keeps the new data that is not persistent in permanent storage.
Block Cache placed in the top of Region Server stores the frequently data in the memory.
MemStore is the write cache storing all the incoming data before keeping it in the permanent memory. Each column family within a region has one MemStore.
HFile stored in HDFS keeps the actual cells on the disk.
Describe “WAL” in HBase?
Ans. Write-Ahead Log (WAL) is a file attached to every individual Region Server within the distributed environment. The WAL stores the new data that is not persisted in the permanent storage and is used if any failure arises to retrieve the data sets.
What is Apache Spark?
Apache Spark is a framework for real-time data analytics in a distributed computing environment executing in-memory calculations to enhance the speed of data processing. It is much faster than MapReduce for large-scale data processing.
What is Apache ZooKeeper and Apache Oozie?
RDD stands for Resilient Distribution Datasets. It is a fault-tolerant composition of elements that run parallelly. The partitioned data in RDD is a crucial component of Apache Spark.
How do you configure an “Oozie” job in Hadoop?
Apache ZooKeeper coordinates with varied services in a distributed environment, and it saves a lot of time by synchronizing and configuring maintenance. Apache Oozie schedules Hadoop jobs and keeps them together as one logical work.
Differentiate between Hadoop 1 and Hadoop 2. While addressing this question, you need to focus on two main points, i.e. Passive NameNode and YARN architecture.
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We understand that with employees there can be a performance issue and we are always open to discuss and find a solution mutually. Typically, if the performance of a resource is not satisfactory then we endeavor to find a replacement for you. In another situation where a hired resources has delivered a decent performance but has been unable to scale it up, then we will charge you for the number of days the resource has worked and, will refund the remaining amount. We would request you inform of any such dissatisfaction within the span of a week so that we can take appropriate steps. In situations where you have not expressed your dissatisfaction and the resource has delivered considerable amount of work, we will not be responsible. Herein, you are requested to mail us an appropriate notice clearly mentioning the termination of our services.
Yes. If you want to hire an employee, GlobalEmployees requires receipt for the first month’s invoice before the employee actually starts working.
This is because GlobalEmployees enters into a legal contract with the employee you hire. Accordingly, GlobalEmployees is legally liable to provide the employee you hire with a paid notice period. Thus, the fee for the 15 days notice period to terminate our services is required in advance before the employee starts working.
No. There are no hidden charges. The price quoted with each submitted resume is the full and complete cost for the entire service. The only exception is if your employee requires software or hardware that we do not provide and is costly to acquire.
Your employee is entitled to 8 paid holidays in a year. In addition, your employee accumulates one day of paid leave per month. Hence, in one year your employee is entitled to 19 days of paid days off work. Any other days off work are unpaid leaves, for which you will not be charged.
No. GlobalEmployees provides you with a long-term dedicated employee. You work with the same employee every day. Hence, it is not possible to cover the odd absence by an employee. In case your employee requires a substantial time off work, GlobalEmployees can replace the employee.
Your employee will be provided with a new desktop. In addition, your employee will have access to all other computer peripherals such as printer, scanner, fax, headsets, web cams, etc.
Yes. Please speak with one of our managers to check if your additional requirements can be met free of any additional charge.
The entire hiring process takes around 1-2 weeks from the time you submit the requirements. In case you are in a rush to find your employee and start sooner, you could subscribe to our Premium service, which would prioritize your case. The turnaround time with Premium service is around 4-6 business days.
The cost of subscribing to our premium service is $100. This charge would be adjusted in your first month’s invoice, thus it is not an extra charge. And yes, the amount is fully refundable if we are unable to find a suitable candidate for you.
Yes. We can discuss the terms once you submit the requirement.
PayPal, Credit Card, Google Checkout, Wire Transfer.
No headaches about infrastructure, labor/employment laws, HR costs, additional employee benefits, etc.
A developer dedicated to your work. 1 Project for 1 Employee policy.
An employee that you choose, not one that’s dumped on you.
Complete Control: Since you are the one driving your work you get a customized development to your specified guidelines.
No Risk: If the employee doesn’t work out, you can get out with a short notice without worrying about severance pay, damages or any other legal hassles.