I have a doubt that in which cases , MapReduce is chosen over hive or pig.
I know that it is used when
We need indepth filtering of the input data.
working with unstructured data.
Working with graph. ....
But is there any place where we cant use hive, pig or we can work much better with MapReduce and it is used highly in real projects
Hive and Pig are generic solutions and they will have overhead while processing the data. Most of the scenarios it is negligible but in some cases it can be considerable.
If there are many tables that needs to be joined, using Hive and Pig tries to apply generic solution, if you use map reduce after understanding the data, you can come up with more optimal solution.
However map reduce should be treated as kernel. If your solution can be reused else where, it will be better to develop it using map reduce and integrate with Hive/Pig/Sqoop.
Pig can be used to process unstructured data. It will give more flexibility than Hive while processing the data.
Bare MapReduce is not written very often these days. Higher level abstractions such as the two you mentioned are more popular and adequate for query workloads.
Even in scenarios where HiveQL is too restrictive one might seek alternatives such as Cascading or Scalding for low-level batch jobs or the ever more popular Spark.
A primary motivation of using these high level abstractions is because most applications require a sequence of map and reduce phase which the MapReduce APIs leave you on your own to figure out how to serialize data between tasks.
I'm having a lot of trouble grasping what exactly a 'Hadoop ecosystem' is conceptually. I understand that you have some data processing tasks that you want to run and so you use MapReduce to split the job up into smaller pieces but I'm unsure about what people mean when they say 'Hadoop Ecosystem'. I'm also unclear as to what the benefits of Apache Spark are and why this is seen as so revolutionary? If it's all in-memory calculation, wouldn't that just mean that you would need higher RAM machines to run Spark jobs? How is Spark different than writing some parallelized Python code or something of that nature.
Your question is rather broad - the Hadoop ecosystem is a wide range of technologies that either support Hadoop MapReduce, make it easier to apply, or otherwise interact with it to get stuff done.
Examples:
The Hadoop Distributed Filesystem (HDFS) stores data to be processed by MapReduce jobs, in a scalable redundant distributed fashion.
Apache Pig provides a language, Pig Latin, for expressing data flows that are compiled down into MapReduce jobs
Apache Hive provides an SQL-like language for querying huge datasets stored in HDFS
There are many, many others - see for example https://hadoopecosystemtable.github.io/
Spark is not all in-memory; it can perform calculations in-memory if enough RAM is available, and can spill data over to disk when required.
It is particularly suitable for iterative algorithms, because data from the previous iteration can remain in memory. It provides a very different (and much more concise) programming interface, compared to plain Hadoop. It can provide some performance advantages even when the work is mostly done on disk rather than in-memory. It supports streaming as well as batch jobs. It can be used interactively, unlike Hadoop.
Spark is relatively easy to install and play with, compared to Hadoop, so I suggest you give it a try to understand it better - for experimentation it can run off a normal filesystem and does not require HDFS to be installed. See the documentation.
I know that MapReduce is a great framework for batch processing on Hadoop. But, Spark also can be used as batch framework on Hadoop that provides scalability, fault tolerance and high performance compared MapReduce. Cloudera, Hortonworks and MapR started supporting Spark on Hadoop with YARN as well.
But, a lot of companies are still using MapReduce Framework on Hadoop for batch processing instead of Spark.
So, I am trying to understand what are the current challenges of Spark to be used as batch processing framework on Hadoop?
Any thoughts?
Spark is an order of magnitude faster than mapreduce for iterative algorithms, since it gets a significant speedup from keeping intermediate data cached in the local JVM.
With Spark 1.1 which primarily includes a new shuffle implementation (sort-based shuffle instead of hash-based shuffle), a new network module (based on netty instead of using block manager for sending shuffle data), a new external shuffle service made Spark perform the fastest PetaByte sort (on 190 nodes with 46TB RAM) and TeraByte sort breaking Hadoop's old record.
Spark can easily handle the dataset which are order of magnitude larger than the cluster's aggregate memory. So, my thought is that Spark is heading in the right direction and will eventually get even better.
For reference this blog post explains how databricks performed the petabyte sort.
I'm assuming when you say Hadoop you mean HDFS.
There are number of benefits of using Spark over Hadoop MR.
Performance: Spark is at least as fast as Hadoop MR. For iterative algorithms (that need to perform number of iterations of the same dataset) is can be a few orders of magnitude faster. Map-reduce writes the output of each stage to HDFS.
1.1. Spark can cache (depending on the available memory) this intermediate results and therefore reduce latency due to disk IO.
1.2. Spark operations are lazy. This means Spark can perform certain optimizing before it starts processing the data because it can reorder operations because they have executed yet.
1.3. Spark keeps a lineage of operations and recreates the partial failed state based on this lineage in case of failure.
Unified Ecosystem: Spark provides a unified programming model for various types of analysis - batch (spark-core), interactive (REPL), streaming (spark-streaming), machine learning (mllib), graph processing (graphx), SQL queries (SparkSQL)
Richer and Simpler API: Spark's API is richer and simpler. Richer because it supports many more operations (e.g., groupBy, filter ...). Simpler because of the expressiveness of these functional constructs. Spark's API supports Java, Scala and Python (for most APIs). There is experimental support for R.
Multiple Datastore Support: Spark supports many data stores out of the box. You can use Spark to analyze data in a normal or distributed file system, HDFS, Amazon S3, Apache Cassandra, Apache Hive and ElasticSearch to name a few. I'm sure support for many other popular data stores is comings soon. This essentially if you want to adopt Spark you don't have to move your data around.
For example, here is what code for word count looks in Spark (Scala).
val textFile = sc.textFile("some file on HDFS")
val wordCounts = textFile.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey((a, b) => a + b)
I'm sure you have to write a few more lines if you are using standard Hadoop MR.
Here are some common misconceptions about Spark.
Spark is just a in-memory cluster computing framework. However, this is not true. Spark excels when your data can fit in memory because memory access latency is lower. But you can make it work even when your dataset doesn't completely fit in memory.
You need to learn Scala to use Spark. Spark is written in Scala and runs on the JVM. But the Spark provides support for most of the common APIs in Java and Python as well. So you can easily get started with Spark without knowing Scala.
Spark does not scale. Spark is for small datasets (GBs) only and doesn't scale to large number of machines or TBs of data. This is also not true. It has been used successfully to sort PetaBytes of data
Finally, if you do not have a legacy codebase in Hadoop MR it makes perfect sense to adopt Spark, the simple reason being all major Hadoop vendors are moving towards Spark for good reason.
Apache Spark runs in memory, making it much faster than mapreduce.
Spark started as a research project at Berkeley.
Mapreduce use disk extensively (for external sort, shuffle,..).
As the input size for a hadoop job is in order of terabytes. Spark memory requirements will be more than traditional hadoop.
So basically, for smaller jobs and with huge memory in ur cluster, sparks wins. And this is not practically the case for most clusters.
Refer to spark.apache.org for more details on spark
We have a system that is made up of multiple PostgreSQL databases. Each database has the same tables, i.e., schema, but only carries a share of the data (and not the full data!).The reason for distributing the data is that our customers run queries that are rather complex and perform up to 100 calculations per row.
By distributing the data to multiple databases, we want to lower the amount of work processed by each database, and ultimately speed up search. At the end, we combine the results of each database to create the final results.
A friend of mine has recommended looking at MapReduce (Hadoop). In my opinion, map-reduce only makes sense if the single workers share the same data but perform different type of work on it (corresponds to multiple instruction, single data).
In our case, however, the workers should perform the same task, but perform that task on various data (corresponds to single instruction, multiple data).
Does MapReduce (Hadoop) make sense for the paradigm same task executed on different data?
Does MapReduce (Hadoop) make sense for the paradigm same task executed on different data?
Yes.
I think you have a misconception about Hadoop and MapReduce. A MapReduce job does indeed work on the same type of data (i.e., "same tables"), but different segments of that data. The parallel Map and Reduce tasks are the same tasks over different portions of the data. MapReduce is most definitely "single instruction, multiple data" from your definition.
Hadoop is by no means a drop-in replacement for a SQL database. They do different things in different ways. Here are some other things to note:
Note that MapReduce is only really going to do batch analytics for you. Things like rollups and counts and aggregates. You won't be able to retrieve or search with MapReduce effectively. Also, updating data in Hadoop is not a typical way you want to do things-- you treat things as more "append only". For any of that, you'll probably want to look at HBase.
Hadoop's file system segments the data for you. From a file system perspective, it'll look like files in folders that contain CSV (or some other file format). Files get split up into blocks, which can then be operated on separately with map tasks. You won't have to manually shard the data like you are now.
Take a look at Hive. It's a abstraction layer on top of MapReduce that interprets a light version of SQL into MapReduce under the covers. It should allow you to convert some of your logic a bit easier.
What are the benefits of using either Hadoop or HBase or Hive ?
From my understanding, HBase avoids using map-reduce and has a column oriented storage on top of HDFS. Hive is a sql-like interface for Hadoop and HBase.
I would also like to know how Hive compares with Pig.
MapReduce is just a computing framework. HBase has nothing to do with it. That said, you can efficiently put or fetch data to/from HBase by writing MapReduce jobs. Alternatively you can write sequential programs using other HBase APIs, such as Java, to put or fetch the data. But we use Hadoop, HBase etc to deal with gigantic amounts of data, so that doesn't make much sense. Using normal sequential programs would be highly inefficient when your data is too huge.
Coming back to the first part of your question, Hadoop is basically 2 things: a Distributed FileSystem (HDFS) + a Computation or Processing framework (MapReduce). Like all other FS, HDFS also provides us storage, but in a fault tolerant manner with high throughput and lower risk of data loss (because of the replication). But, being a FS, HDFS lacks random read and write access. This is where HBase comes into picture. It's a distributed, scalable, big data store, modelled after Google's BigTable. It stores data as key/value pairs.
Coming to Hive. It provides us data warehousing facilities on top of an existing Hadoop cluster. Along with that it provides an SQL like interface which makes your work easier, in case you are coming from an SQL background. You can create tables in Hive and store data there. Along with that you can even map your existing HBase tables to Hive and operate on them.
While Pig is basically a dataflow language that allows us to process enormous amounts of data very easily and quickly. Pig basically has 2 parts: the Pig Interpreter and the language, PigLatin. You write Pig script in PigLatin and using Pig interpreter process them. Pig makes our life a lot easier, otherwise writing MapReduce is always not easy. In fact in some cases it can really become a pain.
I had written an article on a short comparison of different tools of the Hadoop ecosystem some time ago. It's not an in depth comparison, but a short intro to each of these tools which can help you to get started.
(Just to add on to my answer. No self promotion intended)
Both Hive and Pig queries get converted into MapReduce jobs under the hood.
HTH
I implemented a Hive Data platform recently in my firm and can speak to it in first person since I was a one man team.
Objective
To have the daily web log files collected from 350+ servers daily queryable thru some SQL like language
To replace daily aggregation data generated thru MySQL with Hive
Build Custom reports thru queries in Hive
Architecture Options
I benchmarked the following options:
Hive+HDFS
Hive+HBase - queries were too slow so I dumped this option
Design
Daily log Files were transported to HDFS
MR jobs parsed these log files and output files in HDFS
Create Hive tables with partitions and locations pointing to HDFS locations
Create Hive query scripts (call it HQL if you like as diff from SQL) that in turn ran MR jobs in the background and generated aggregation data
Put all these steps into an Oozie workflow - scheduled with Daily Oozie Coordinator
Summary
HBase is like a Map. If you know the key, you can instantly get the value. But if you want to know how many integer keys in Hbase are between 1000000 and 2000000 that is not suitable for Hbase alone.
If you have data that needs to be aggregated, rolled up, analyzed across rows then consider Hive.
Hopefully this helps.
Hive actually rocks ...I know, I have lived it for 12 months now... So does HBase...
Hadoop is a a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models.
There are four main modules in Hadoop.
Hadoop Common: The common utilities that support the other Hadoop modules.
Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.
Hadoop YARN: A framework for job scheduling and cluster resource management.
Hadoop MapReduce: A YARN-based system for parallel processing of large data sets.
Before going further, Let's note that we have three different types of data.
Structured: Structured data has strong schema and schema will be checked during write & read operation. e.g. Data in RDBMS systems like Oracle, MySQL Server etc.
Unstructured: Data does not have any structure and it can be any form - Web server logs, E-Mail, Images etc.
Semi-structured: Data is not strictly structured but have some structure. e.g. XML files.
Depending on type of data to be processed, we have to choose right technology.
Some more projects, which are part of Hadoop:
HBase™: A scalable, distributed database that supports structured data storage for large tables.
Hive™: A data warehouse infrastructure that provides data summarization and ad-hoc querying.
Pig™: A high-level data-flow language and execution framework for parallel computation.
Hive Vs PIG comparison can be found at this article and my other post at this SE question.
HBASE won't replace Map Reduce. HBase is scalable distributed database & Map Reduce is programming model for distributed processing of data. Map Reduce may act on data in HBASE in processing.
You can use HIVE/HBASE for structured/semi-structured data and process it with Hadoop Map Reduce
You can use SQOOP to import structured data from traditional RDBMS database Oracle, SQL Server etc and process it with Hadoop Map Reduce
You can use FLUME for processing Un-structured data and process with Hadoop Map Reduce
Have a look at: Hadoop Use Cases.
Hive should be used for analytical querying of data collected over a period of time. e.g Calculate trends, summarize website logs but it can't be used for real time queries.
HBase fits for real-time querying of Big Data. Facebook use it for messaging and real-time analytics.
PIG can be used to construct dataflows, run a scheduled jobs, crunch big volumes of data, aggregate/summarize it and store into relation database systems. Good for ad-hoc analysis.
Hive can be used for ad-hoc data analysis but it can't support all un-structured data formats unlike PIG.
Consider that you work with RDBMS and have to select what to use - full table scans, or index access - but only one of them.
If you select full table scan - use hive. If index access - HBase.
Understanding in depth
Hadoop
Hadoop is an open source project of the Apache foundation. It is a framework written in Java, originally developed by Doug Cutting in 2005. It was created to support distribution for Nutch, the text search engine. Hadoop uses Google's Map Reduce and Google File System Technologies as its foundation.
Features of Hadoop
It is optimized to handle massive quantities of structured, semi-structured and unstructured data using commodity hardware.
It has shared nothing architecture.
It replicates its data into multiple computers so that if one goes down, the data can still be processed from another machine that stores its replica.
Hadoop is for high throughput rather than low latency. It is a batch operation handling massive quantities of data; therefore the response time is not immediate.
It complements Online Transaction Processing and Online Analytical Processing. However, it is not a replacement for a RDBMS.
It is not good when work cannot be parallelized or when there are dependencies within the data.
It is not good for processing small files. It works best with huge data files and data sets.
Versions of Hadoop
There are two versions of Hadoop available :
Hadoop 1.0
Hadoop 2.0
Hadoop 1.0
It has two main parts :
1. Data Storage Framework
It is a general-purpose file system called Hadoop Distributed File System (HDFS).
HDFS is schema-less
It simply stores data files and these data files can be in just about any format.
The idea is to store files as close to their original form as possible.
This in turn provides the business units and the organization the much needed flexibility and agility without being overly worried by what it can implement.
2. Data Processing Framework
This is a simple functional programming model initially popularized by Google as MapReduce.
It essentially uses two functions: MAP and REDUCE to process data.
The "Mappers" take in a set of key-value pairs and generate intermediate data (which is another list of key-value pairs).
The "Reducers" then act on this input to produce the output data.
The two functions seemingly work in isolation with one another, thus enabling the processing to be highly distributed in highly parallel, fault-tolerance and scalable way.
Limitations of Hadoop 1.0
The first limitation was the requirement of MapReduce programming expertise.
It supported only batch processing which although is suitable for tasks such as log analysis, large scale data mining projects but pretty much unsuitable for other kinds of projects.
One major limitation was that Hadoop 1.0 was tightly computationally coupled with MapReduce, which meant that the established data management vendors where left with two opinions:
Either rewrite their functionality in MapReduce so that it could be
executed in Hadoop or
Extract data from HDFS or process it outside of Hadoop.
None of the options were viable as it led to process inefficiencies caused by data being moved in and out of the Hadoop cluster.
Hadoop 2.0
In Hadoop 2.0, HDFS continues to be data storage framework.
However, a new and seperate resource management framework called Yet Another Resource Negotiater (YARN) has been added.
Any application capable of dividing itself into parallel tasks is supported by YARN.
YARN coordinates the allocation of subtasks of the submitted application, thereby further enhancing the flexibility, scalability and efficiency of applications.
It works by having an Application Master in place of Job Tracker, running applications on resources governed by new Node Manager.
ApplicationMaster is able to run any application and not just MapReduce.
This means it does not only support batch processing but also real-time processing. MapReduce is no longer the only data processing option.
Advantages of Hadoop
It stores data in its native from. There is no structure imposed while keying in data or storing data. HDFS is schema less. It is only later when the data needs to be processed that the structure is imposed on the raw data.
It is scalable. Hadoop can store and distribute very large datasets across hundreds of inexpensive servers that operate in parallel.
It is resilient to failure. Hadoop is fault tolerance. It practices replication of data diligently which means whenever data is sent to any node, the same data also gets replicated to other nodes in the cluster, thereby ensuring that in event of node failure,there will always be another copy of data available for use.
It is flexible. One of the key advantages of Hadoop is that it can work with any kind of data: structured, unstructured or semi-structured. Also, the processing is extremely fast in Hadoop owing to the "move code to data" paradigm.
Hadoop Ecosystem
Following are the components of Hadoop ecosystem:
HDFS: Hadoop Distributed File System. It simply stores data files as close to the original form as possible.
HBase: It is Hadoop's database and compares well with an RDBMS. It supports structured data storage for large tables.
Hive: It enables analysis of large datasets using a language very similar to standard ANSI SQL, which implies that anyone familier with SQL should be able to access data on a Hadoop cluster.
Pig: It is an easy to understand data flow language. It helps with analysis of large datasets which is quite the order with Hadoop. Pig scripts are automatically converted to MapReduce jobs by the Pig interpreter.
ZooKeeper: It is a coordination service for distributed applications.
Oozie: It is a workflow schedular system to manage Apache Hadoop jobs.
Mahout: It is a scalable machine learning and data mining library.
Chukwa: It is data collection system for managing large distributed system.
Sqoop: It is used to transfer bulk data between Hadoop and structured data stores such as relational databases.
Ambari: It is a web based tool for provisioning, managing and monitoring Hadoop clusters.
Hive
Hive is a data warehouse infrastructure tool to process structured data in Hadoop. It resides on top of Hadoop to summarize Big Data and makes querying and analyzing easy.
Hive is not
A relational database
A design for Online Transaction Processing (OLTP).
A language for real-time queries and row-level updates.
Features of Hive
It stores schema in database and processed data into HDFS.
It is designed for OLAP.
It provides SQL type language for querying called HiveQL or HQL.
It is familier, fast, scalable and extensible.
Hive Architecture
The following components are contained in Hive Architecture:
User Interface: Hive is a data warehouse infrastructure that can create interaction between user and HDFS. The User Interfaces that Hive supports are Hive Web UI, Hive Command line and Hive HD Insight(In Windows Server).
MetaStore: Hive chooses respective database servers to store the schema or Metadata of tables, databases, columns in a table, their data types and HDFS mapping.
HiveQL Process Engine: HiveQL is similar to SQL for querying on schema info on the Metastore. It is one of the replacements of traditional approach for MapReduce program. Instead of writing MapReduce in Java, we can write a query for MapReduce and process it.
Exceution Engine: The conjunction part of HiveQL process engine and MapReduce is the Hive Execution Engine. Execution engine processes the query and generates results as same as MapReduce results. It uses the flavor of MapReduce.
HDFS or HBase: Hadoop Distributed File System or HBase are the data storage techniques to store data into file system.
For a Comparison Between Hadoop Vs Cassandra/HBase read this post.
Basically HBase enables really fast read and writes with scalability. How fast and scalable? Facebook uses it to manage its user statuses, photos, chat messages etc. HBase is so fast sometimes stacks have been developed by Facebook to use HBase as the data store for Hive itself.
Where As Hive is more like a Data Warehousing solution. You can use a syntax similar to SQL to query Hive contents which results in a Map Reduce job. Not ideal for fast, transactional systems.
I worked on Lambda architecture processing Real time and Batch loads.
Real time processing is needed where fast decisions need to be taken in case of Fire alarm send by sensor or fraud detection in case of banking transactions.
Batch processing is needed to summarize data which can be feed into BI systems.
we used Hadoop ecosystem technologies for above applications.
Real Time Processing
Apache Storm: Stream Data processing, Rule application
HBase: Datastore for serving Realtime dashboard
Batch Processing
Hadoop: Crunching huge chunk of data. 360 degrees overview or adding context to events. Interfaces or frameworks like Pig, MR, Spark, Hive, Shark help in computing. This layer needs scheduler for which Oozie is good option.
Event Handling layer
Apache Kafka was first layer to consume high velocity events from sensor.
Kafka serves both Real Time and Batch analytics data flow through Linkedin connectors.
First of all we should get clear that Hadoop was created as a faster alternative to RDBMS. To process large amount of data at a very fast rate which earlier took a lot of time in RDBMS.
Now one should know the two terms :
Structured Data : This is the data that we used in traditional RDBMS and is divided into well defined structures.
Unstructured Data : This is important to understand, about 80% of the world data is unstructured or semi structured. These are the data which are on its raw form and cannot be processed using RDMS. Example : facebook, twitter data. (http://www.dummies.com/how-to/content/unstructured-data-in-a-big-data-environment.html).
So, large amount of data was being generated in the last few years and the data was mostly unstructured, that gave birth to HADOOP. It was mainly used for very large amount of data that takes unfeasible amount of time using RDBMS. It had many drawbacks, that it could not be used for comparatively small data in real time but they have managed to remove its drawbacks in the newer version.
Before going further I would like to tell that a new Big Data tool is created when they see a fault on the previous tools. So, whichever tool you will see that is created has been done to overcome the problem of the previous tools.
Hadoop can be simply said as two things : Mapreduce and HDFS. Mapreduce is where the processing takes place and HDFS is the DataBase where data is stored. This structure followed WORM principal i.e. write once read multiple times. So, once we have stored data in HDFS, we cannot make changes. This led to the creation of HBASE, a NOSQL product where we can make changes in the data also after writing it once.
But with time we saw that Hadoop had many faults and for that we created different environment over the Hadoop structure. PIG and HIVE are two popular examples.
HIVE was created for people with SQL background. The queries written is similar to SQL named as HIVEQL. HIVE was developed to process completely structured data. It is not used for ustructured data.
PIG on the other hand has its own query language i.e. PIG LATIN. It can be used for both structured as well as unstructured data.
Moving to the difference as when to use HIVE and when to use PIG, I don't think anyone other than the architect of PIG could say. Follow the link :
https://developer.yahoo.com/blogs/hadoop/comparing-pig-latin-sql-constructing-data-processing-pipelines-444.html
Let me try to answer in few words.
Hadoop is an eco-system which comprises of all other tools. So, you can't compare Hadoop but you can compare MapReduce.
Here are my few cents:
Hive: If your need is very SQLish meaning your problem statement can be catered by SQL, then the easiest thing to do would be to use Hive. The other case, when you would use hive is when you want a server to have certain structure of data.
Pig: If you are comfortable with Pig Latin and you need is more of the data pipelines. Also, your data lacks structure. In those cases, you could use Pig. Honestly there is not much difference between Hive & Pig with respect to the use cases.
MapReduce: If your problem can not be solved by using SQL straight, you first should try to create UDF for Hive & Pig and then if the UDF is not solving the problem then getting it done via MapReduce makes sense.
Pig: it is better to handle files and cleaning data
example: removing null values,string handling,unnecessary values
Hive: for querying on cleaned data
1.We are using Hadoop for storing Large data (i.e.structure,Unstructure and Semistructure data ) in the form file format like txt,csv.
2.If We want columnar Updations in our data then we are using Hbase tool
3.In case of Hive , we are storing Big data which is in structured format
and in addition to that we are providing Analysis on that data.
4.Pig is tool which is using Pig latin language to analyze data which is in any format(structure,semistructure and unstructure).
Cleansing Data in Pig is very easy,a suitable approach would be cleansing data through pig and then processing data through hive and later uploading it to hdfs.
Use of Hive, Hbase and Pig w.r.t. my real time experience in different projects.
Hive is used mostly for:
Analytics purpose where you need to do analysis on history data
Generating business reports based on certain columns
Efficiently managing the data together with metadata information
Joining tables on certain columns which are frequently used by using bucketing concept
Efficient Storing and querying using partitioning concept
Not useful for transaction/row level operations like update, delete, etc.
Pig is mostly used for:
Frequent data analysis on huge data
Generating aggregated values/counts on huge data
Generating enterprise level key performance indicators very frequently
Hbase is mostly used:
For real time processing of data
For efficiently managing Complex and nested schema
For real time querying and faster result
For easy Scalability with columns
Useful for transaction/row level operations like update, delete, etc.
Short answer to this question is -
Hadoop - Is Framework which facilitates distributed file system and programming model which allow us to store humongous sized data and process data in distributed fashion very efficiently and with very less processing time compare to traditional approaches.
(HDFS - Hadoop Distributed File system)
(Map Reduce - Programming Model for distributed processing)
Hive - Is query language which allows to read/write data from Hadoop distributed file system in a very popular SQL like fashion. This made life easier for many non-programming background people as they don't have to write Map-Reduce program anymore except for very complex scenarios where Hive is not supported.
Hbase - Is Columnar NoSQL Database. Underlying storage layer for Hbase is again HDFS. Most important use case for this database is to be able to store billion's of rows with million's of columns. Low latency feature of Hbase helps faster and random access of record over distributed data, is very important feature to make it useful for complex projects like Recommender Engines. Also it's record level versioning capability allow user to store transactional data very efficiently (this solves the problem of updating records we have with HDFS and Hive)
Hope this is helpful to quickly understand the above 3 features.
I believe this thread hasn't done in particular justice to HBase and Pig in particular. While I believe Hadoop is the choice of the distributed, resilient file-system for big-data lake implementations, the choice between HBase and Hive is in particular well-segregated.
As in, a lot of use-cases have a particular requirement of SQL like or No-SQL like interfaces. With Phoenix on top of HBase, though SQL like capabilities is certainly achievable, however, the performance, third-party integrations, dashboard update are a kind of painful experiences. However, it's an excellent choice for databases requiring horizontal scaling.
Pig is in particular excellent for non-recursive batch like computations or ETL pipelining (somewhere, where it outperforms Spark by a comfortable distance). Also, it's high-level dataflow implementations is an excellent choice for batch querying and scripting. The choice between Pig and Hive is also pivoted on the need of the client or server-side scripting, required file formats, etc. Pig supports Avro file format which is not true in the case of Hive. The choice for 'procedural dataflow language' vs 'declarative data flow language' is also a strong argument for the choice between pig and hive.
Hadoop:
HDFS stands for Hadoop Distributed File System which uses Computational processing model Map-Reduce.
HBase:
HBase is Key-Value storage, good for reading and writing in near real time.
Hive:
Hive is used for data extraction from the HDFS using SQL-like syntax. Hive use HQL language.
Pig:
Pig is a data flow language for creating ETL. It's an scripting language.
Pig is mostly dead after Cloudera got rid of it in CDP. Also last release on Apache was 19 June, 2017: release 0.17.0 so basically no committers actively working anymore. Use Spark or Python way more powerful than Pig.