Hadoop vs Vertica

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Our analysts compared Hadoop vs Vertica based on data from our 400+ point analysis of Big Data Analytics Tools, user reviews and our own crowdsourced data from our free software selection platform.

Hadoop Software Tool
Vertica Software Tool

Product Basics

Apache Hadoop is an open source framework for dealing with large quantities of data. It’s considered a landmark group of products in the business intelligence and data analytics space, and is comprised of several different components. It functions on basic analytics principles like distributed computing, large data processing, machine learning and more.

Hadoop is part of a growing family of free, open source software (FOSS) projects from the Apache Foundation, and works well in conjunction with other third-party products.
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Vertica is an analytics and data exploration platform designed to ingest massive quantities of data, parse it, and then return business insights as reports and interactive graphics. Elastically scalable, it provides batch as well as streaming analytics with massively parallel processing, ANSI-compliant SQL querying and ACID transactions.

Deployable in the cloud, on-premise, on Apache Hadoop and as a hybrid model, its resource manager enables concurrent job runs with reduced CPU and memory usage and data compression for storage optimization. A serverless setup and advanced data trawling techniques help users store and access their data with ease.
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Product Insights

  • Scalability: Hadoop's distributed computing model allows it to scale up from a single server to thousands of machines, each offering local computation and storage. This means businesses can handle more data simply by adding more nodes to the network, making it highly adaptable to the exponential growth of data.
  • Cost-Effectiveness: Unlike traditional relational database management systems that can be prohibitively expensive to scale, Hadoop enables businesses to store and manage vast amounts of data at a fraction of the cost, thanks to its ability to run on commodity hardware.
  • Flexibility: Hadoop is designed to efficiently process large volumes of data of different types, from structured to unstructured. This flexibility allows organizations to harness the power of big data without the constraints of a predefined schema, making it easier to make data-driven decisions.
  • Fault Tolerance: Hadoop automatically replicates data to multiple nodes, ensuring that the system is highly resilient to hardware failure. If a node goes down, tasks are automatically redirected to other nodes to ensure continuous operation, minimizing downtime and data loss.
  • Processing Speed: With its unique storage method based on a distributed file system that maps data wherever it is located on a cluster, Hadoop can process large volumes of data much more quickly than traditional systems. This speed makes it ideal for applications that require processing terabytes or petabytes of data, such as analyzing customer behavior patterns.
  • Efficient Data Processing: Hadoop's MapReduce programming model is designed for processing large data sets in parallel across a distributed cluster, which significantly speeds up the data processing tasks. This efficiency is crucial for performing complex calculations and analytics on big data in a timely manner.
  • Community Support: Being an open-source framework, Hadoop benefits from a vast community of developers and users who continuously contribute to its development and improvement. This community support ensures that Hadoop stays at the forefront of big data processing technology, with regular updates and a wide range of compatible tools and extensions.
  • Data Locality Optimization: Hadoop moves computation closer to data rather than moving large data sets across the network to be processed. This approach reduces the time taken to process data, as it minimizes network congestion and increases the overall throughput of the system.
  • Improved Business Continuity: The fault tolerance and high availability features of Hadoop ensure that businesses can maintain continuous operations, even in the face of hardware failures or other issues. This reliability is critical for organizations that depend on real-time data analysis for operational decision-making.
  • Enhanced Data Security: Hadoop includes robust security features, such as Kerberos authentication, to ensure that data is protected against unauthorized access. This security framework is essential for businesses that handle sensitive information, providing peace of mind that their data is secure.
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  • Improve Decision Making: Get business insights like workforce metrics, retail information, vital proprietary intel and usage data through data ingestion from diverse sources. Its high-level overview of diverse data pools empowers data-driven business decisions. 
  • Self-Service Analytics:  Provide self-service reporting and analysis for users of all skill levels — from non-technical users to analysts — at a more sophisticated level, using custom formulas and algorithms. 
  • Big Data Insights: Get intel and insights through large data silos to enrich business decisions and diagnose pain points or attach victories to actionable data. 
  • Gain a Competitive Advantage: Take on competitors and optimize business operations through data-informed decision-making. 
  • Flexible Licensing: Buy the product outright, purchase a subscription or pay by the hour with its flexible licensing. Hourly usage comes with full support and requires no commitment. 
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  • Distributed Computing: Also known as the Hadoop Distributed File System (HDFS), this feature can easily spread computing tasks across multiple nodes, providing faster processing and data redundancy in the event that there’s a critical failure. Hadoop is the industry standard for big data analytics. 
  • Fault Tolerance: Data is replicated across nodes, so even in the event of one node failing, the data is left intact and retrievable. 
  • Scalability: The app is able to run on less robust hardware or scale up to industrial data processing servers with ease. 
  • Integration With Existing Systems: Because Hadoop is so central to so many big data analytics applications, it integrates easily into a number of commercial platforms like Google Analytics and Oracle Big Data SQL or with other Apache software like YARN and MapR. 
  • In-Memory Processing: Hadoop, in conjunction with Apache Spark, is able to quickly parse and process large quantities of data by storing it in-memory. 
  • Hadoop MapR: MapR is a component of Hadoop that combines a number of features like redundancy, POSIX compliance and more into a single, enterprise grade component that looks like a standard file server. 
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  • Streaming Analytics: Connects to Apache Kafka for IoT data analysis in real time. Analyzes and manages large volumes of data from IoT devices such as machine and sensor data for buildings, vehicles, medical systems, smart devices and wearables. 
  • Machine Learning: Get automated insights and deliverables through machine learning modules that automatically digest and parse large data portions. ML modules are built into its core — no need to pay for them or install them separately. 
  • Software Only: Work with a robust software interface with dedicated IT resources. All data warehousing, storage and processing infrastructure is hosted offsite. 
  • Fast SQL Databases: Store and retrieve data through highly scalable and speedy SQL databases. 
  • Massively Parallel Processing: Get increased speed and scalability at larger scales by running two processes side-by-side through massively parallel processing. 
  • Columnar Storage: Read only the most important sets of data first through columnar storage that greatly speeds up data retrieval. 
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#31

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User Sentiment Summary

Great User Sentiment 474 reviews
Great User Sentiment 203 reviews
85%
of users recommend this product

Hadoop has a 'great' User Satisfaction Rating of 85% when considering 474 user reviews from 3 recognized software review sites.

88%
of users recommend this product

Vertica has a 'great' User Satisfaction Rating of 88% when considering 203 user reviews from 3 recognized software review sites.

4.3 (101)
4.5 (108)
4.3 (244)
n/a
n/a
4.3 (65)
4.2 (129)
4.2 (30)

Synopsis of User Ratings and Reviews

Scalability: Hadoop can store and process massive datasets across clusters of commodity hardware, allowing businesses to scale their data infrastructure as needed without significant upfront investments.
Cost-Effectiveness: By leveraging open-source software and affordable hardware, Hadoop provides a cost-effective solution for managing large datasets compared to traditional enterprise data warehouse systems.
Flexibility: Hadoop's ability to handle various data formats, including structured, semi-structured, and unstructured data, makes it suitable for diverse data analytics tasks.
Resilience: Hadoop's distributed architecture ensures fault tolerance. Data is replicated across multiple nodes, preventing data loss in case of hardware failures.
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Data Processing: All users who mention computing say that the tool’s columnar storage and parallel processing enable faster querying.
Performance: Almost 72% of the users who review performance say the platform is robust and reliable with high availability.
Functionality: Around 56% of the users who review functionality say that it is feature-rich and performs as expected.
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Complexity: Hadoop can be challenging to set up and manage, especially for organizations without a dedicated team of experts. Its ecosystem involves numerous components, each requiring configuration and integration.
Security Concerns: Hadoop's native security features are limited, often necessitating additional tools and protocols to ensure data protection and compliance with regulations.
Performance Bottlenecks: While Hadoop excels at handling large datasets, it may not be the best choice for real-time or low-latency applications due to its batch-oriented architecture.
Cost Considerations: Implementing and maintaining a Hadoop infrastructure can be expensive, particularly for smaller organizations or those with limited IT budgets.
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Cost: All users who mention cost say that data storage limits can be restrictive and the tool is expensive.
Community Support: Citing lack of technical community support, approximately 50% of the users say that it makes adoption difficult.
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Hadoop has been making waves in the Big Data Analytics scene, and for good reason. Users rave about its ability to scale like a champ, handling massive datasets that would make other platforms sweat. Its flexibility is another major plus, allowing it to adapt to different data formats and processing needs without breaking a sweat. And let's not forget about reliability – Hadoop is built to keep on chugging even when things get rough. However, it's not all sunshine and rainbows. Some users find Hadoop's complexity a bit daunting, especially if they're new to the Big Data game. The learning curve can be steep, so be prepared to invest some time and effort to get the most out of it. So, who's the ideal candidate for Hadoop? Companies dealing with mountains of data, that's who. If you're in industries like finance, healthcare, or retail, where data is king, Hadoop can be your secret weapon. It's perfect for tasks like analyzing customer behavior, detecting fraud, or predicting market trends. Just remember, Hadoop is a powerful tool, but it's not a magic wand. You'll need a skilled team to set it up and manage it effectively. But if you're willing to put in the work, Hadoop can help you unlock the true potential of your data.

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Vertica Analytics is a big data relational database that provides batch as well as streaming analytics to enterprises. Citing a robust, distributed architecture with massively parallel processing (MPP), all users who review data processing say that it performs extremely fast computing with I/O optimization, and columnar storage makes it ideal for reporting. Approximately 72% of the users who review performance say that it is a reliable tool with high availability and virtually no downtime, with K-safety protocol in place for efficient fault tolerance. Citing its feature set, around 56% of the users say that they are satisfied with its elastic scalability, rich analytical functions and excellent clustering technology. On the flip side, almost 50% of the users who mention technical and community support say that it is inadequate and possibly contributes to the platform’s steep learning curve. All users who review its cost say that the solution is expensive, with restrictive data storage limits. In summary, Vertica is a big data and analytics platform that provides streaming analytics with lightning-fast query speeds, machine learning and forecast capabilities.

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