Apache Hadoop provides a scalable, cost-effective open-source platform capable of handling vast data volumes with features like HDFS, distributed processing, and high integration capabilities.
| Product | Mindshare (%) |
|---|---|
| Apache Hadoop | 3.3% |
| Snowflake | 9.3% |
| Teradata | 8.8% |
| Other | 78.6% |
| Title | Rating | Mindshare | Recommending | |
|---|---|---|---|---|
| Dell PowerStore | 4.4 | 1.4% | 97% | 187 interviewsAdd to research |
| Teradata | 4.1 | 8.8% | 88% | 83 interviewsAdd to research |
| Company Size | Count |
|---|---|
| Small Business | 11 |
| Midsize Enterprise | 7 |
| Large Enterprise | 16 |
| Company Size | Count |
|---|---|
| Small Business | 64 |
| Midsize Enterprise | 39 |
| Large Enterprise | 150 |
Apache Hadoop is known for its distributed file system HDFS, which supports large data volumes efficiently. Its open-source nature allows cost-effective scalability and compatibility with tools like Spark for enhanced analytics. While it offers significant processing power, areas for improvement include user-friendliness, interface design, security measures, and real-time data handling. Users benefit from data storage for structured and unstructured data, facilitated by its distributed processing architecture. Data replication ensures fault tolerance, while its capability to integrate with tools like Apache Atlas and Talend highlights its versatility.
What are the key features of Apache Hadoop?Industries leverage Apache Hadoop for Big Data analytics, data lakes, ETL tasks, and enterprise data hubs, handling unstructured and structured data from IoT, RDBMS, and real-time streams. Its applications extend to data warehousing, AI/ML projects, and data migration, employing tools like Apache Ranger, Hive, and Talend for effective data management and analysis.
| Author info | Rating | Review Summary |
|---|---|---|
| Financial Advisor at a financial services firm with 10,001+ employees | 4.0 | We had a limited on-premises deployment of Apache Hadoop which scaled well and was reliable, but maintaining it was challenging due to a lack of resources and expertise after the original team left. We prefer solutions with structured support. |
| Principle Network and Database Engr at Parsons Corporation | 4.5 | I use Apache Hadoop daily to analyze unstructured incident data, benefiting from its AI and machine learning capabilities, strong failover support, and fault tolerance, especially useful in our dual-server setup and field environments prone to hardware failures. |
| Database Administrator at Lacoste | 4.5 | I am working on migrating a customer's data warehouse from Oracle to Hadoop, specifically considering Cloudera, due to its data warehouse capabilities and scalability. The integration of reporting tools like Power BI with Hadoop poses some challenges. |
| Software developer at Fiserv | 4.0 | I use Apache Hadoop in my company for its efficient analytical processing and organized data distribution. However, it struggles with incremental data processing and has high licensing costs. Setup and technical support need improvement for better user experience and resolution times. |
| Head of Data at a energy/utilities company with 51-200 employees | 4.5 | Apache Hadoop's distributed computing capability efficiently accelerates data processing by distributing tasks across multiple nodes. While it offers cost savings on compute resources through optimization, the availability of comprehensive training materials could be improved to enhance onboarding and skill development. |
| Senior Assosiate Consultant at Applied Materials | 3.0 | I use Apache Hadoop for data storage and report generation, appreciating its open-source nature, ability to handle large data volumes, and effectiveness in data processing and storage. However, limited support requires deeper knowledge and improvisation. |
| IT Support Specialist at Convergys Corporation | 4.5 | We used Apache Hadoop mainly for data analysis and storage, benefiting from its low cost, open-source nature, and efficient performance on commodity hardware. While its flexibility and resilience are advantageous, improved security measures would enhance its capabilities. |
| Head Of Data Governance at Alibaba Group | 4.0 | We use the Hadoop File System for big data due to its open-source nature and cost-effectiveness. However, dealing with data skewness requires custom solutions, unlike Spark, which is more efficient and faster due to in-memory processing. |
