Apache HBase

Website

hbase.apache.org

$ Details

Categories

#Databases #NoSQL Databases #Development #Key-Value Database

Uber Engineering

Website

eng.uber.com

$ Details

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Categories

#AI #Data Science And Machine Learning #Developer Tools #Machine Learning

Apache HBase features and specs

• Scalability
  HBase is designed to scale horizontally, allowing it to handle large amounts of data by adding more nodes. This makes it suitable for applications requiring high write and read throughput.
• Consistency
  It provides strong consistency for reads and writes, which ensures that any read will return the most recently written value. This is crucial for applications where data accuracy is essential.
• Integration with Hadoop Ecosystem
  HBase integrates seamlessly with Hadoop and other components like Apache Hive and Apache Pig, making it a suitable choice for big data processing tasks.
• Random Read/Write Access
  Unlike HDFS, HBase supports random, real-time read/write access to large datasets, making it ideal for applications that need frequent data updates.
• Schema Flexibility
  HBase provides a flexible schema model that allows changes on demand without major disruptions, supporting dynamic and evolving data models.

Possible disadvantages of Apache HBase

• Complexity
  Setting up and managing HBase can be complex and may require expert knowledge, especially for tuning and optimizing performance in large-scale deployments.
• High Latency for Small Queries
  While HBase is designed for large-scale data, small queries can suffer from higher latency due to the overhead of its distributed nature.
• Sparse Documentation
  Despite being widely used, HBase documentation and community support can sometimes be lacking, making issue resolution difficult for new users.
• Dependency on Hadoop
  Since HBase depends heavily on the Hadoop ecosystem, issues or limitations with Hadoop components can affect HBase’s performance and functionality.
• Limited Transaction Support
  HBase lacks full ACID transaction support, which can be a limitation for applications needing complex transactional processing.

Uber Engineering features and specs

• Innovative Solutions
  Uber Engineering works on cutting-edge technologies and innovative solutions to complex problems, offering engineers the opportunity to tackle challenging and impactful projects.
• Scalable Systems
  The team is known for its ability to create scalable and robust systems that handle millions of transactions and users worldwide, providing valuable experience in high-volume system architecture.
• Diverse Technical Areas
  Uber Engineering covers a wide range of technical domains including distributed systems, data science, AI and machine learning, which allows engineers to broaden their expertise.
• Open Source Contributions
  Uber Engineering often contributes to the open-source community, which can enhance public visibility and offers engineers the opportunity to contribute to and improve widely-used software.

Possible disadvantages of Uber Engineering

• High Pressure Environment
  Working in a fast-paced, high-pressure environment can lead to stress and burnout for some engineers, as there is often a strong focus on rapid delivery and continuous improvement.
• Complex Legacy Systems
  Engineers may need to work with complex legacy systems, which can be difficult to manage and update, potentially hindering innovation and requiring significant maintenance work.
• Rapid Change
  Frequent changes in technology strategy and product focus can make it challenging to have a long-term impact, requiring engineers to be adaptable and open to shifting priorities.
• Resource Intensive
  Building and maintaining large-scale systems is resource-intensive in terms of both time and computational power, which can lead to constraints and bottlenecks that need to be managed effectively.

Apache HBase 101: How HBase Can Help You Build Scalable, Distributed Java Applications

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