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HBase vs Oracle: What are the differences?

Introduction

HBase and Oracle are both popular database systems used for storing and managing large amounts of data. However, there are several key differences between the two.

  1. Data Model: HBase is a NoSQL database, while Oracle is a relational database. This means that HBase stores data in a distributed and scalable manner, organized in columns, column families, and rows. On the other hand, Oracle stores data in tables and enforces a predefined schema, allowing for complex relationships between different tables.

  2. Scalability: HBase is designed to scale horizontally, meaning that it can easily handle large amounts of data by adding more servers to the cluster. Oracle, on the other hand, can scale vertically by upgrading the hardware of a single server. This difference in scalability makes HBase a better choice for big data applications that require massive amounts of storage and processing power.

  3. Consistency: HBase provides eventual consistency, which means that reads may not always return the most up-to-date data. This is because HBase is designed for high availability and fault tolerance, allowing for concurrent updates from multiple clients. In contrast, Oracle provides strong consistency, ensuring that every read operation returns the most recent data.

  4. Transaction Support: Oracle is known for its strong transaction support, allowing for ACID (Atomicity, Consistency, Isolation, Durability) properties. It provides features like rollback, commit, and concurrency control, ensuring data integrity. HBase, on the other hand, does not natively support complex transactions, and only offers atomicity on a per-row basis.

  5. Query Language: Oracle uses SQL (Structured Query Language) for querying and manipulating data, which is a widely adopted standard language for relational databases. HBase, on the other hand, provides a Java API for accessing and manipulating data. While SQL allows for complex queries and joins, the HBase API requires developers to write code to perform similar operations.

  6. Cost: Oracle is a commercial database that requires licensing fees, while HBase is an open-source project that is free to use. This cost difference can be a significant factor for organizations with budget constraints, especially when dealing with large-scale data storage and processing requirements.

In Summary, HBase and Oracle differ in their data models, scalability, consistency, transaction support, query language, and cost. The choice between the two depends on the specific requirements of the application, such as the size of the dataset, the need for strong consistency, the complexity of transactions, and the available budget.

Advice on HBase and Oracle
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HBaseHBaseMilvusMilvus
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RocksDBRocksDB

I am researching different querying solutions to handle ~1 trillion records of data (in the realm of a petabyte). The data is mostly textual. I have identified a few options: Milvus, HBase, RocksDB, and Elasticsearch. I was wondering if there is a good way to compare the performance of these options (or if anyone has already done something like this). I want to be able to compare the speed of ingesting and querying textual data from these tools. Does anyone have information on this or know where I can find some? Thanks in advance!

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You've probably come to a decision already but for those reading...here are some resources we put together to help people learn more about Milvus and other databases https://zilliz.com/comparison and https://github.com/zilliztech/VectorDBBench. I don't think they include RocksDB or HBase yet (you could could recommend on GitHub) but hopefully they help answer your Elastic Search questions.

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Decisions about HBase and Oracle
Daniel Moya
Data Engineer at Dimensigon · | 4 upvotes · 459.3K views

We have chosen Tibero over Oracle because we want to offer a PL/SQL-as-a-Service that the users can deploy in any Cloud without concerns from our website at some standard cost. With Oracle Database, developers would have to worry about what they implement and the related costs of each feature but the licensing model from Tibero is just 1 price and we have all features included, so we don't have to worry and developers using our SQLaaS neither. PostgreSQL would be open source. We have chosen Tibero over Oracle because we want to offer a PL/SQL that you can deploy in any Cloud without concerns. PostgreSQL would be the open source option but we need to offer an SQLaaS with encryption and more enterprise features in the background and best value option we have found, it was Tibero Database for PL/SQL-based applications.

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We wanted a JSON datastore that could save the state of our bioinformatics visualizations without destructive normalization. As a leading NoSQL data storage technology, MongoDB has been a perfect fit for our needs. Plus it's open source, and has an enterprise SLA scale-out path, with support of hosted solutions like Atlas. Mongo has been an absolute champ. So much so that SQL and Oracle have begun shipping JSON column types as a new feature for their databases. And when Fast Healthcare Interoperability Resources (FHIR) announced support for JSON, we basically had our FHIR datalake technology.

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In the field of bioinformatics, we regularly work with hierarchical and unstructured document data. Unstructured text data from PDFs, image data from radiographs, phylogenetic trees and cladograms, network graphs, streaming ECG data... none of it fits into a traditional SQL database particularly well. As such, we prefer to use document oriented databases.

MongoDB is probably the oldest component in our stack besides Javascript, having been in it for over 5 years. At the time, we were looking for a technology that could simply cache our data visualization state (stored in JSON) in a database as-is without any destructive normalization. MongoDB was the perfect tool; and has been exceeding expectations ever since.

Trivia fact: some of the earliest electronic medical records (EMRs) used a document oriented database called MUMPS as early as the 1960s, prior to the invention of SQL. MUMPS is still in use today in systems like Epic and VistA, and stores upwards of 40% of all medical records at hospitals. So, we saw MongoDB as something as a 21st century version of the MUMPS database.

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Pros of HBase
Pros of Oracle
  • 9
    Performance
  • 5
    OLTP
  • 1
    Fast Point Queries
  • 44
    Reliable
  • 33
    Enterprise
  • 15
    High Availability
  • 5
    Hard to maintain
  • 5
    Expensive
  • 4
    Maintainable
  • 4
    Hard to use
  • 3
    High complexity

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Cons of HBase
Cons of Oracle
    Be the first to leave a con
    • 14
      Expensive

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    - No public GitHub repository available -

    What is HBase?

    Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.

    What is Oracle?

    Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.

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    Blog Posts

    Jun 24 2020 at 4:42PM

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    What are some alternatives to HBase and Oracle?
    Cassandra
    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
    Google Cloud Bigtable
    Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    Hadoop
    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
    Druid
    Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
    See all alternatives