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Hadoop HDFS description
Hadoop HDFS is the foundation for storing vast amounts of data across a network of computers. It's like a giant, shared file system, designed to handle massive datasets that wouldn't fit on a single machine. HDFS ensures data reliability and accessibility, even if individual computers fail. It's commonly used by large organizations dealing with big data challenges, enabling them to efficiently store and process information for analysis and decision-making.
Who is Hadoop HDFS best for
Hadoop HDFS excels at storing massive datasets for large organizations. Its distributed system ensures data reliability and accessibility, even with hardware failures. Users praise its scalability and cost-effectiveness but note the complex setup and steep learning curve. It's a powerful tool for large-scale data storage and processing, but requires technical expertise.
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Best fit for medium to large companies handling extensive datasets.
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Ideal for software, IT, telecommunications, and education sectors.
Hadoop HDFS features
| Supported Hadoop HDFS supports various unstructured data like text, images, and video. |
| Supported Hadoop HDFS supports data partitioning through tools like Hive, enabling efficient querying by dividing data into smaller chunks. |
| Supported Hadoop HDFS is designed for high availability and scaling horizontally to handle traffic spikes. |
| Supported Hadoop HDFS supports tiered storage for hot, warm, and cold data based on access patterns. |
| Supported Hadoop HDFS excels at importing large files due to its distributed storage and tools like Sqoop and Flume. |
We evaluate the sentiment that users express about non-functional aspects of the software
Ease of Use
Reliability and Performance
Scalability
Hadoop HDFS reviews
We've summarised 140 Hadoop HDFS reviews (Hadoop HDFS G2 reviews) and summarised the main points below.
- Excellent for handling and processing massive datasets.
- Cost-effective solution for storing large volumes of data on commodity hardware.
- Highly scalable and fault-tolerant, ensuring data availability.
- Supports various data formats and integrates well with other Hadoop ecosystem tools.
- Large and active community support with plentiful documentation available.
- Complex setup and configuration can be time-consuming.
- Steep learning curve, especially for those unfamiliar with distributed systems.
- Not suitable for real-time or interactive analysis.
- Performance issues with small files and high latency for small file operations.
- Requires expertise in Linux and command-line interface.
Hadoop HDFS alternatives
Databricks Data Intelligence PlatformBetter for users comfortable with cloud-based platforms. More suitable for organizations focused on data science, machine learning, and AI-driven solutions. Has more momentum in terms of popularity and company growth. Better for those seeking a unified data and AI platform.Read more
Azure Data Lake StoreBetter suited for cloud-native environments and integrates seamlessly with other Azure services. It offers potentially simpler setup and better scalability for some users. Azure Data Lake Store is growing faster and has more momentum.Read more
Google Cloud StorageBetter for a wider range of industries and use cases like data lakes and AI data storage. Offers more flexible storage options and pricing plans. Easier to use and implement, with seamless integration with other Google services. A better Hadoop HDFS alternative for those seeking cloud-based storage. Comes with free credits for new users.Read more
SnowflakeBetter for data warehousing and business intelligence. A better Hadoop HDFS alternative for SQL-based data transformation, broader industry applicability, and faster growth. Easier to use and navigate, with seamless integration with various data tools. Suitable for businesses of all sizes.Read more
Apache SqoopSpecifically designed for transferring data between Hadoop and relational databases. Better for structured data and ETL processes. Open-source.Read more
SAP IQBetter for complex queries and reporting with columnar storage. Handles large datasets efficiently but has a steeper learning curve and potentially difficult pricing.Read more
Hadoop HDFS FAQ
What is Hadoop HDFS and what does Hadoop HDFS do?
Hadoop HDFS is a distributed file system designed to store and process massive datasets across a cluster of commodity hardware. It provides high availability, scalability, and fault tolerance, making it ideal for big data analytics. HDFS excels at handling large files and various data formats.
How does Hadoop HDFS integrate with other tools?
Hadoop HDFS integrates with other tools within the Hadoop ecosystem, such as Hive for data partitioning and Sqoop and Flume for large file imports. It also supports various data formats, enhancing its compatibility for diverse data processing needs.
What the main competitors of Hadoop HDFS?
Top alternatives to Hadoop HDFS include cloud-based data lakes like Azure Data Lake Store and Databricks, data warehouses such as Snowflake, and other big data platforms. These competitors offer various features like scalability, data processing, and analytics capabilities.
Is Hadoop HDFS legit?
Hadoop HDFS is a legitimate and widely used storage system for big data. It's known for its scalability and fault tolerance, enabling reliable access to massive datasets. However, it's important to be aware of the complexity and resource intensiveness for optimal performance.
How much does Hadoop HDFS cost?
Hadoop HDFS is an open-source software and is free to use. However, the cost of implementing and maintaining HDFS depends on factors like hardware, infrastructure, and support. It's worth exploring the product for its powerful distributed storage capabilities.
Is Hadoop HDFS customer service good?
Hadoop HDFS users highlight the vibrant community support as a positive aspect of customer service. However, some users have expressed concerns about complexities in configuration and a steep learning curve, potentially indicating areas for improvement in customer service and documentation.
Reviewed by
Michal has worked at startups for many years and writes about topics relating to software selection and IT management. As a former consultant for Bain, a business advisory company, he also knows how to understand needs of any business and find solutions to its problems.
Tymon is a seasoned CTO who loves finding the perfect tools for any task. He recently headed up the tech department at Batmaid, a well-known Swiss company, where he managed about 60 software purchases, including CX, HR, Payroll, Marketing automation and various developer tools.