Hadoop is made up of several main modules:HDFS is a special file system for working with large filesYARN is a task scheduler responsible for managing the resources of the computing cluster, as well as the MapReduce module and the module for managing Hadoop internal libraries.
Its specific use cases include data searching, data analysis, data reporting, large-scale indexing of files (e.
g.
, log files or data from web crawlers), and other data processing tasks using what’s colloquially known in the developing world as Big Data.
Is it a perfect tool?Although Hadoop came roaring into an extremely big mainstream and became the number one Big Data tool, it is still far away from an ideal solution to all problems.
For instance, I will not recommend using this tool for a relational database due to slow response times.
What is more, Hadoop will also show its bad side in case of working with a real-time data analysis, General Network file system and non-parallel data processing.
So, making bets exclusively at Hadoop will not bring you pure satisfaction.
Don’t fear to waste some time on an exploration of other tools.
Download Link: https://hadoop.
apache.
org/releases.
html2.
Elasticsearch: Average Volume Data King (and a Hadoop killer?)Elasticsearch is a search engine with json rest api using Lucene.
Similar engines are used for complex search in the database of documents.
For example, search taking into account the morphology of the language or search by geo coordinates.
Official clients are available in Java, NET (C #), Python, Groovy, JavaScript, PHP, Perl, Ruby.
Elasticsearch is developed by Elastic along with related projects called Elastic Stack — Elasticsearch, Logstash, Beats, and Kibana.
Unlike traditional relational databases that store data in tables, ElasticSearch uses a key-value store for objects and is a lot more versatile.
It can run queries way more complex than traditional databases and do this at petabyte scale.
Features:If the scale of your project is still not large enough for the implementation of large platforms like Hadoop, you can take advantage of options based on standard NoSQL-solutions that will allow you to cope with the accumulation and processing of data of average volume.
Such solutions include Elasticsearch.
It is incredibly great for working within a certain amount of data (2–10 terabytes per year, 20–30 billion documents in indices), and also integrates well with the Spark cluster.
Agents (Beats) help on a specific device or a specific server to collect information that interests users of the system.
With the help of these agents, you can collect various kinds of data: Windows system information from the log, logs of the Linux operating system, data on the Android OS, analyze traffic from the device itself, be it TCP, HTTP, etc.
Due to a number of advantages, you may ask ‘Why do people use Hadoop when there is ElasticSearch?’.
But, the truth is, this question is like ‘why do we need automobiles when we have air conditioning?’.
Elasticsearch is great at what it does, but it’s no analytics platform.
Its unforgivable sin of streaming data loss during ingestion and arduous data ETL process make it unsuitable as the foundation of an analytics pipeline.
Download Link: https://www.
elastic.
co/downloads/elasticsearch3.
Pentaho Will Turn Big Data into Big InsightsAlthough this name tends to be associated with a sweet dream of a 17-year-old hacker, this tool provides you a bit different but significant things.
Pentaho combines both analytic processing and data integration that makes attaining results quicker.
What is more, its built-in integration with IoT endpoints and unique metadata injection functionality speeds data collection from multiple sources.
Features:In overall, this tool is really good at data access and integration for effective data visualization.
It empowers users to architect big data at the source and streams them for accurate analytics.
Pentaho allows checking data with easy access to analytics, including charts, visualizations, and reporting.
Plus, it supports a wide spectrum of big data sources by offering unique capabilities.
Download Link: http://www.
pentaho.
com/download4.
Talend: Develop More Quickly with Less Ramp-Up TimeTalend is considered to be the next-generation leader in cloud and big data integration software.
At its core, it is an open source software integration platform/vendor which offers data integration and data management solutions.
Its graphical wizard generates native code.
It also allows big data integration, master data management and checks data quality.
Features:Accelerate time to value for big data projectsSimplify ETL & ELT for big dataTalend Big Data Platform simplifies using MapReduce and Spark by generating native codeSmarter data quality with machine learning and natural language processingAgile DevOps to speed up big data projectsDownload Link: https://www.
talend.
com/download/5.
Lumify — Simplistic and Excellent ToolLumify is a big data fusion, analysis, and visualization platform.
Lumify is possibly the choice for those pouring over the 11 million-plus document dump commonly known as the Panama Papers.
It helps users to discover connections and explore relationships in their data via a suite of analytic options.
Features:Personally, I do not consider it the best tool for big data.
But basically it is worth attention for a number of features:It provides both 2D and 3D graph visualizations with a variety of automatic layoutsIt provides a variety of options for analyzing the links between entities on the graphIt comes with specific ingest processing and interface elements for textual content, images, and videosIt spaces feature allows you to organize work into a set of projects, or workspacesIt is built on proven, scalable big data technologiesDownload link: http://www.
altamiracorp.
com/index.
php/lumify/6.
Skytree: Machine Learning Meets Big DataSkytree is a big data analytics tool that empowers data scientists to build more accurate models faster.
It offers accurate predictive machine learning models that are easy to use.
It is a general-purpose platform that allows big data specialists to focus on what matters most, which Skytree says is Mean Time to Insights (MTI), and focus on what they are good at building and deploying analytic models rather than coding algorithms.
Features:Highly Scalable AlgorithmsArtificial Intelligence for Data ScientistsIt allows data scientists to visualize and understand the logic behind ML decisionsSkytree via the easy-to-adopt GUI or programmatically in JavaModel InterpretabilityIt is designed to solve robust predictive problems with data preparation capabilitiesDownload link: http://www.
skytree.
net/7.
Presto (SQL Query Engine)Presto is a distributed open-source mechanism of the SQL query engine for performing interactive analytical queries to data sources of various sizes: from gigabytes to petabytes.
Roughly speaking, this is a system for interactive analytics of Big Data.
By the way, it was developed from scratch by Facebook and is notable for the speed of work characteristic of commercial data warehouses.
Features:A single Presto query can aggregate data from multiple sources, allowing you to conduct Big Data analysis across an organization.
Presto supports ANSI SQL, which means that in addition to JSON, ARRAY, MAP, and ROW, you can use standard SQL data types, window interface functionality, statistical and approximative aggregate functions.
Compared to Hadoop, Presto has a drawback: more active participation in the development, construction, and deployment of user-defined functions.
However, for me, Presto is one of the best open source mechanisms for analyzing Big Data.
I am sure that it will be so for you.
8.
RapidMiner: Extract Big Value from Big DataAnother good thing worth your attention.
RapidMiner is a free open-source environment for predictive analytics that has a full arsenal of necessary functions.
The system supports all stages of in-depth data analysis, including the resulting visualization, validation, and optimization.
Features:The great advantage I would like to highlight is that in order to use RapidMiner you do not need to know programming.
Here the principle of visual programming is implemented.
You do not need to write code as well as you do not need to carry out complex mathematical calculations.
Everything happens as follows: the user drops the data onto the working field, and then simply drags the operators into the GUI, forming the data processing process.
It is possible to understand the generated code, but in most cases, it is not necessary.
This platform for analyzing Big Data is “friendly” with Hadoop, however, if you use the RapidMiner Radoop paid extension.
The extension requires the Hadoop cluster to be accessible from the client running RapidMiner Studio.
Download Link: https://rapidminer.
com/get-started/9.
Knime — Another Free Data Mining SystemKNIME offers an intuitive working environment without the need for programming.
Features:If toughing text analysis, this platform allows you to perform the following tasks:Intersection: minimizing variations of key terms into original forms.
Stopword filtering: remove minor words.
Splitting into lexemes: splitting text lines into smaller units, for example, words and phrases, through user-specified rules.
KNIME can also read the information directly from Twitter and work with unstructured files like CSV volumes.
In addition, there is deep learning, web analysis, image processing, analysis of social networks and more.
However, RapidMiner is still a simpler analytical platform for a beginner, because it automatically generates detailed assumptions about the possible reasons for the lack of connection of operators.
Each node is well described in KNIME, but there are no explanations why there are no operators connected.
Finally, the functionality of RapidMiner in terms of word processing is currently wider.
Thus, RapidMiner is more suitable for newcomers, and advanced specialists who have tried all systems to analyze Big Data can find something interesting in KNIME.
Download Link: https://www.
knime.
com/knime-big-data-extensions10.
R Programming Environment: Last but Not Least Must-HaveWhen it comes to Big Data Analysis, it’s probably impossible to ignore one more wonderful tool under the title of R.
This language is hugely popular among statisticians and data miners for developing statistical software and data analysis.
At its heart, R is a programming language and free software environment supported by the R Foundation for Statistical Computing.
Features:R is mostly used along with JuPyteR stack (Julia, Python, R) for enabling wide-scale statistical analysis and data visualization.
JupyteR Notebook is one of 4 most popular Big Data visualization tools, as it allows composing literally any analytical model from more than 9,000 CRAN (Comprehensive R Archive Network) algorithms and modules, running it in a convenient environment, adjusting it on the go and inspecting the analysis results at once.
For what is it good for?.Let’s start with compilation and running on a wide variety of UNIX platforms, Windows and MacOS which makes its usage really comfortable.
Another great advantage is that R can run inside the SQL server.
But most importantly, R supports Apache Hadoop and Spark and it easily scales from a single test machine to vast Hadoop data lakes.
Download Link: https://www.
r-project.
org/Final Thoughts for Big Data DudesThe Dude just is.
Lazy when he is or motivated when he is.
As I’ve always said a good expert is fundamentally a lazy expert who knows how to work around things.
But, does it mean you should choose one tool and use it successfully for all purposes?Well, let me answer these questions as short and succinct as possible.
Just don’t expect to find perfect tools to manage the entire analytics pipeline and put the whole mission on them.
Your way of thinking is a rug that really ties the room together if you know what I mean.
You can deploy any one of these tools mentioned above.
Having defined your goal, you will easily select the right tool (or set of tools) that will allow you to conduct a complete data analysis.
Now, briefly summing up all the above analysis, we have the next results:If simple aggregations is what you need, and your data volumes aren’t extreme — stick to Elasticseach.
If you’re building an analytics team, and plan to ask complex questions to improve your marketing or product decision — you will probably need something else: Hadoop, if you have enough technical expertise; or Presto and RapidMiner if you want to get fast results and not worry about infrastructure.
Do I miss anything?.Disagree entirely?.Share your opinion in the comments!Feel free to follow me on Medium and Instagram.