15 Open Source Big Data Tools That Professionals Are Using In 2019

In the last few years, big data analysis has gained a lot of importance. Now companies have huge chunks of data and that information is growing at a steady pace every day.

Thanks to the emergence of social media and other technologies like the internet of things there is no dearth of data for marketers. However, all the data is pointless if you cannot analyze and find out details out of it.

In today’s time, open source big data tools help companies do just that. To assist we have brought together a list of Top 10 Open Source Big Data Tools That Professionals Are Using In 2019.

Using any of these tools you can start any project with ease as there is no fear of any complications arising due to data migration later on.

Go through our analysis and find out which one is the right choice for the kind of data analysis you are planning to do.

 

1. Hadoop

Hadoop

 Apache Hadoop is a collection of open-source software utilities that facilitate using a network of many computers to solve problems involving massive amounts of data and computation. It provides a software framework for distributed storage and processing of big data using the MapReduce programming model

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Review: TrustRadius

Hadoop has been an amazing development in the world of big data. Where relational databases fall short with regard to tuning and performance, Hadoop rises to the occasion and allows for massive customization leveraging the different tools and modules. We use Hadoop to input raw data and add layers of consolidation or analysis to make business decisions about disparate data points.

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2. HPCC

Textvectors are an easy and effective way to analyze your previously inaccessible textual data. In place (supporting distributed linear algebra) predictive modeling functionality to perform linear regression, logistic regression, decision trees, and random forests.

Extract, transform, and load your data using a powerful scripting language (ECL) specifically developed to work with data. An index based search engine to perform real-time queries.

Soap, XML, rest, and SQL are all supported interfaces. Data profiling, data cleansing, snapshot data updates and consolidation, job scheduling and automation are some of the key features.

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Review: glassdoor

I worked at HPCC Systems (Less than a year)

Pros

Very prompt in replying to queries Give a lot of opportunities Open to discussions and ideas

Cons

Nothing I can think of

Advice To Management

Keep it up!

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3. Storm

Apache Storm is a free and open source distributed real-time computation system. Storm makes it easy to reliably process unbounded streams of data, doing for real-time processing what Hadoop did for batch processing. The storm is simple, can be used with any programming language, and is a lot of fun to use!

The storm has many use cases: real-time analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. The storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant guarantees your data will be processed, and is easy to set up and operate.

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Review: G2

What do you like best?
If you are needing real-time computing on most newer servers these days, apache storm has it covered, as one of the most reliable frameworks for batch processing.

What do you dislike?
I have used many apache products but getting this set up seemed to take much longer around 4hrs. After figuring everything out we were recommended a list of installation guides and support we wish we had prior.

Recommendations to others considering the product
If you have never used Apache before we recommend finding an authorized installer to set up as this would most likely speed up the process.

What business problems are you solving with the product? What benefits have you realized?
Realtime Server Accusation responses. Speeding up network response time from multiple branches within our network of over 200 systems.

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4. Statwing

Statwing was built by and for analysts, so you can clean data, explore relationships, and create charts in minutes instead of hours. There is no faster or more delightful way to work with data, even if you’re already an expert with spreadsheets (like most of our customers).

Asking a simple question of your data in a spreadsheet takes minutes of shuffling data, creating charts and pivot tables, and writing formulas. Traditional statistical software was built decades ago for statisticians, so it requires technical expertise to ask even simple questions.

And unlike traditional software, statwing accounts for data issues like outliers, so you can always be confident in your analyses. Statwing also translates results in plain English, so analysts unfamiliar with statistical analysis can still get its benefits.

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Review: Capterra

Comments: I used to use spss to do this kind of analysis. I didn’t think it was that bad at the time, but after using statwing I’m blown away by the difference. It’s just a lot easier to use, much more intuitive, and I get things done much more quickly.

The only knock is that it doesn’t have more sophisticated analyses like PCA or survival analysis. But I personally only needed the basic + regression analysis, so it worked well for me.

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5. Cassandra

cassandra.apache

The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance.

Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data.

Cassandra’s support for replicating across multiple datacenters is best-in-class, providing lower latency for your users and the peace of mind of knowing that you can survive regional outages.

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Review: G2

What do you dislike?
Weak Read performance and stability during automated tasks (table compression etc) – if heap gets over the limit, the node just crashes and don’t respawn.

Recommendations to others considering the product
Take into account, that each Cassandra node requires a decent amount of system requirements. About 4 GB of RAM is usually minimal, so running it in a cloud can be very costly. Second thing – do you have enough data? Cassandra is designed to store a lot of data, however, it lacks in other aspects. If you have very little data, other NoSQL options may be a lot better.

What business problems are you solving with the product? What benefits have you realized?
Storing vast amounts of meta information. It is intended to be quickly received and queried

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6. Pentaho

The term big data applies to very large, complex, or dynamic datasets that need to be stored and managed over a long time. To derive benefits from big data, you need the ability to access, process, and analyze data as it is being created.

However, the size and structure of big data make it very inefficient to maintain and process it using traditional relational databases.

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Review: TrustRadius

Pentaho is the primary source of business intelligence in my company. We have many Pentaho users across the whole organization that is making good use of Pentaho Analyzer to create the reports. At the reported side, it helps the business users to evaluate all the work that has been done by the different teams across the organizations.

By seeing the reports they can check, for example, how many issues are open, how many bugs have been fixed in a specific duration, how many builds failed in which phase, etc.

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7. Cloudera

Cloudera

Deutsche Telekom improved operational efficiencies by 50 per cent with a modern data platform. Komatsu used data and analytics from connected equipment to double efficiency and improve safety.FireEye increased advanced persistent threat research productivity by 60 per cent.

 

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Review: FinancesOnline

Cloudera offers the world the best data platform constructed on Hadoop. By that, it means Cloudera has the fastest, easiest, and most secure data platforms designed to solve even the most complex business issues and challenges when it comes to data.

Big data is essential in today’s business landscape. It provides businesses with great information and insights that help drive success. With Cloudera, users can build an enterprise data hub and truly leverage the power of data by unlocking its hidden value. On top of that, Cloudera allows users to add the security, governance, and management functions that are required to create an enterprise-grade foundation for data.

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8. Rapidminer

Rapidminer named a leader in the Gartner magic quadrant for data science and machine learning platforms for the sixth year in a row to join the 30,000+ global organizations in every industry who use rapidminer to drive revenue, reduce costs, and avoid risk. Integrate predictive analytics in big tools streamline low-value talks accelerate connectivity to enterprise data.

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Review: Capterra

Pros: easy of use, fast, really nice presentation of results.

Cons: the expansion through code is not easy. It has a lot of functionalities but in some locations, you got stuck and need to implement in another way.

Overall: I combined rapidminer with r and it is a wonderful tool. It is the best in the market to build useful information from the result of data ming process.

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9. Kaggle

kaggle

These micro-courses are the single fastest way to gain the skills you’ll need to do independent data science projects.

Pare down complex topics to their key practical components, so you gain usable skills in a few hours (instead of weeks or months).

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Review: G2

What do you like best?
I love the fact that Kaggle is a community. Users share their work, help in comments and enhance the experience for other users. This allows it to be a very active learning environment.

What do you dislike?
Sometimes it is overwhelming to keep up with all the competitions. I get many email notifications. It might be useful to send you notifications based on your profile or activity.

What business problems are you solving with the product? What benefits have you realized?
Machine learning problems. It allows helping learn methods for model improvement and improvement of accuracy rates. The users sometimes approach unique and creative solutions to increase accuracy in models and that can be applied elsewhere.

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10. Hive

Hive

The apache hive data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. The structure can be projected onto data already in storage. A command line tool and JDBC driver are provided to connect users to a hive.

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Review: techradar

The prospect of turning their regular, boring home into a smart one still seems a little bit scary and very time-consuming. Luckily, the hive active heating 2 is looking to change all of that and make upgrading your home easier, quicker and worth it in the long-run.

One of the main reasons why building up your smart home can seem scary is because there’s an assumption that you have to go ‘all in’ and upgrade everything to really see the benefits. This isn’t how it works. At least not anymore.

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11. Qubole

 Qubole

Qubole’s customers are 100% successful in processing an exabyte of data every month, in a time when 85% of big data projects fail to meet expectations.

  • Qubole has been rated a High Performer by G2 Crowd in the Big Data Processing and Distribution category.
  • Fastest Path To Big Data Success
  • Single Self-Service Platform
  • Sustainable Cloud Economics
  • Massively Scalable on any Cloud

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Review: TrustRadius

Qubole was used across the company to ease migration strategies from an on-premise Hadoop environment into the cloud. The end place for the data was between Amazon services such as S3 and RDS, but the initial goal was to use multiple clouds, as some parts of the company were using Google’s BigQuery.

From what I’ve seen, Qubole abstracts away the setup, scalability, and installation of many Hadoop services by providing an a la carte offering of big data processing services from query engines of Hive, Spark, and Presto to useful UI tools of the query editors and Zeppelin Notebooks.

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12. CouchDB

 CouchDB

Apache CouchDB™ lets you access your data where you need it. The Couch Replication Protocol is implemented in a variety of projects and products that span every imaginable computing environment from globally distributed server-clusters, over mobile phones to web browsers.

Store your data safely, on your own servers, or with any leading cloud provider. Your web- and native applications love CouchDB because it speaks JSON natively and supports binary data for all your data storage needs.

The Couch Replication Protocol lets your data flow seamlessly between server clusters to mobile phones and web browsers, enabling a compelling offline-first user-experience while maintaining high performance and strong reliability. CouchDB comes with a developer-friendly query language, and optionally MapReduce for simple, efficient, and comprehensive data retrieval.

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Review: G2

CouchDB will be easy to pick if you are already familiar with JavaScript and JSON. The data is stored as JSON, and you use MapReduce functions in JavaScript to query the data. This makes it a nice complement to a full-stack JavaScript application running Node and a JavaScript front end.

If it fits the requirements of your application, you can use CouchDB as a REST API, and forgo the need for an additional API implementation on the server.

Easy to get started and good documentation to learn how to use the database. There is a GUI available to easily view your data.

What Do You Dislike?

The GUI that is available is not always intuitive. When I was first gettings started with it there were a few things that did not work as I expected them to that caused frustration.

The MapReduce query method can be hard to adjust to if you are used to traditional SQL databases. It is very powerful but takes some time to get familiar with it.

Recommendations To Others Considering The Product

As with most database decisions, you need to understand the needs of your application. CouchDB is well suited for document storage, but you will probably have an easier time with something else for a typical CRUD app.

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13. Flink

Flink

Apache Flink is an open-source stream processing Big data tool. It is distributed, high-performing, always-available, and accurate data streaming applications.

Features:

  • Provides results that are accurate, even for out-of-order or late-arriving data
  • It is stateful and fault-tolerant and can recover from failures
  • It can perform at a large scale, running on thousands of nodes
  • Has good throughput and latency characteristics
  • This big data tool supports stream processing and windowing with event time semantics
  • It supports flexible windowing based on time, count, or sessions to data-driven windows
  • It supports a wide range of connectors to third-party systems for data sources and sinks

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Review: TrustSpot

I used your Flink Hotspot at a lake house outside Vienna and it worked perfectly. I always had it in my backpack when I went anywhere so I always had internet access. My friend is a doctor and this device allowed her to stay in contact with her staff and patients. I highly recommend using FLINK Hotspot and it will be all I need to stay connected.

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14. Openrefine

Openrefine

OpenRefine (formerly Google Refine) is a powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data.

OpenRefine is available in English, Chinese, Spanish, French, Russian, Portuguese (Brazil), German, Japanese, Italian, Hungarian, Hebrew, Filipino, Cebuano, Tagalog.

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Review: Software advice

OpenRefine is an on-premise data cleaning and quality maintenance tool that serves small, midsized and large enterprises. Formerly known as “Google Refine,” the product was renamed to “OpenRefine” in 2012 when it became open source. Primary features include data import, data cleaning, dataset linking, entity extraction and data documentation.

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15. DataCleaner

 DataCleaner

ataCleaner is a data quality analysis application and a solution platform. It has strong data profiling engine. It is extensible and thereby adds data cleansing, transformations, matching, and merging.

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Review: SOFTPEDIA

DataCleaner is a software application that supports a long list of databases and provides users with a simple means of creating new databases, analyzing them and creating reports based on the information gathered.

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