Hadoop vs BigQuery

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Our analysts compared Hadoop vs BigQuery 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

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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BigQuery, a cloud-based data warehouse offered by Google, provides businesses with a scalable and cost-effective solution for analyzing massive datasets. It eliminates the need for infrastructure management, allowing users to focus on extracting valuable insights from their data using familiar SQL and built-in machine learning capabilities. BigQuery's serverless architecture enables efficient scaling, allowing you to query terabytes of data in seconds and petabytes in minutes.

BigQuery is particularly well-suited for organizations dealing with large and complex datasets that require rapid analysis. Its ability to integrate data from various sources, including Google Cloud Platform and other cloud providers, makes it a versatile tool for businesses with diverse data landscapes. Key benefits include scalability, ease of use, and cost-effectiveness. BigQuery offers a pay-as-you-go pricing model, allowing you to only pay for the resources you consume. You are billed based on the amount of data processed by your queries and the amount of data stored.

While BigQuery offers numerous advantages, it's important to consider factors such as your specific data analytics needs and budget when comparing it to similar products. User experiences with BigQuery have generally been positive, highlighting its speed, scalability, and ease of use. However, some users have noted that the pricing structure can become complex for highly demanding workloads.

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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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  • Forecast and Plan Ahead: Ingest large amounts of data quickly to strengthen forecasting and boost decision-making processes. 
  • Deliver Insights: Find discrepancies in data and act on them accordingly. 
  • Focus on Analytics and Not Infrastructure: Handles large volumes of data without putting strain on an organization’s IT resources. 
  • Provide a User-Friendly Environment: It’s user-friendly for both technical and non-technical users. High-level knowledge is not necessary to operate the software effectively. 
  • Speed Up Processes: Utilizes fast SQL databases to quickly and efficiently analyze terabytes worth of data. 
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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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  • Machine Learning: Comes with machine learning modules that can perform mass-segmentation and recommendations in seconds. These modules can be built and trained within minutes without ingesting data for training. 
  • Cloud Hosted: Handles all the hardware provisioning, warehousing and hardware management from the cloud. 
  • Real-Time Analytics: Large volumes of business data are quickly analyzed and presented to the user to ensure that insights and data discrepancies can be immediately uncovered. 
  • Automated Backups: Data is automatically stored and backed up multiple times a day. Data histories can be easily restored to prevent loss and major changes. 
  • Big Data Ecosystem Integrations: Integrate with other big data products such as Hadoop, Spark and Beam. Data can be directly written from the system into these products. 
  • Data Governance: Features such as access management, filter views, encryption and more are included in the software. The product is compliant with data regulations such as the GDPR. 
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User Sentiment Summary

Great User Sentiment 474 reviews
Excellent User Sentiment 724 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.

90%
of users recommend this product

BigQuery has a 'excellent' User Satisfaction Rating of 90% when considering 724 user reviews from 3 recognized software review sites.

4.3 (101)
4.4 (292)
4.3 (244)
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4.6 (283)
4.2 (129)
4.4 (149)

Awards

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BigQuery stands above the rest by achieving an ‘Excellent’ rating as a User Favorite.

User Favorite Award

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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Performance: The system can execute queries on massive amounts of data with agility, as specified by about 89% of users who mentioned performance.
Functionality: About 68% of users who reviewed functionality talked about its robust inbuilt features.
Ease of Use: The UI is simple and easy to navigate, according to about 72% of users who talked about user-friendliness.
Integration: Approximately 75% of reviewers who talked about integration said that it connects to numerous other tools seamlessly.
Scalability: All users who reviewed scalability said that the platform scales to thousands of servers.
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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: Approximately 76% of users who mentioned cost complained that it’s expensive, and charges can rack up quickly if queries aren’t properly constructed.
Learning Curve: About 82% of users mentioned that the software has a steep learning curve.
Resources: About 89% of users who spoke about resources said that documentation and video tutorials are lacking and need improvement.
Visualization: Data visualization capabilities aren’t up to the mark, according to all users who talked about visualization.
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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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Bigquery is a scalable big data warehouse solution. It enables users to pull correlated data streams using SQL like queries. Queries are executed fast regardless of the size of the datasets. It manages the dynamic distribution of workloads across computational clusters. The easy-to-navigate UI is robust and allows the user to create and execute machine learning models seamlessly. Users liked that it can connect to a variety of data analytics and visualization tools. However, users complained that query optimization is an additional hassle they have to deal with, as the solution is expensive and poorly constructed queries can quickly accumulate charges. It can be overwhelming for the non-technical user, and SQL coding knowledge is required to leverage its data analysis capabilities. Data visualization features are lacking and in need of improvement.

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