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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. Hadoop vs Sybase

Hadoop vs Sybase

OverviewComparisonAlternatives

Overview

Hadoop
Hadoop
Stacks2.7K
Followers2.3K
Votes56
GitHub Stars15.3K
Forks9.1K
Sybase
Sybase
Stacks41
Followers80
Votes10

Hadoop vs Sybase: What are the differences?

Introduction

Hadoop and Sybase are both popular technologies used in the field of big data and analytics. However, they have key differences that set them apart from each other. In this article, we will explore these differences in detail.

  1. Scalability: Hadoop is highly scalable, allowing for the storage and processing of massive amounts of data across a distributed cluster of commodity hardware. Sybase, on the other hand, is not designed to handle the same level of scalability as Hadoop.

  2. Data Processing: Hadoop's primary focus is on batch processing of large data sets. It uses a distributed file system (HDFS) and the MapReduce programming model to efficiently process data. Sybase, on the other hand, is a relational database management system (RDBMS) that supports both online transaction processing (OLTP) and online analytical processing (OLAP).

  3. Data Storage: Hadoop stores data across a distributed file system, while Sybase stores data in a centralized relational database. Hadoop's distributed storage system allows for better fault tolerance and high availability, as data is replicated across multiple nodes. Sybase, on the other hand, provides a more traditional approach to data storage with its centralized database model.

  4. Data Types: Hadoop is designed to handle a wide variety of data types, ranging from structured to semi-structured and unstructured data. This makes it well-suited for processing big data in its various forms. Sybase, being a relational database, is primarily designed to handle structured data.

  5. Data Processing Paradigm: Hadoop follows a parallel processing paradigm, where jobs are divided into smaller tasks that can be executed in parallel across multiple nodes in the cluster. Sybase, on the other hand, follows a more sequential processing paradigm, where queries are executed sequentially.

  6. Ecosystem: Hadoop has a rich and diverse ecosystem of tools and technologies that have been built around it, such as Apache Pig, Apache Hive, and Apache Spark. These tools provide higher-level abstractions and easier programming interfaces for working with Hadoop. Sybase, while having its own ecosystem of tools and technologies, does not have the same level of diversity and popularity as Hadoop.

In summary, Hadoop excels in scalability, data processing for large data sets, handling diverse data types, and has a rich ecosystem of tools. Sybase, on the other hand, is a relational database management system that is suitable for traditional data storage and processing.

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Detailed Comparison

Hadoop
Hadoop
Sybase
Sybase

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.

Modernize and accelerate your transaction-based applications on premise and in the cloud. This high-performance SQL database server uses a relational management model to meet rising demand for performance, reliability, and efficiency in every industry.

-
Faster, more secure transfer of database files; Multiversion concurrency control (MVCC); Three-system monitoring procedures
Statistics
GitHub Stars
15.3K
GitHub Stars
-
GitHub Forks
9.1K
GitHub Forks
-
Stacks
2.7K
Stacks
41
Followers
2.3K
Followers
80
Votes
56
Votes
10
Pros & Cons
Pros
  • 39
    Great ecosystem
  • 11
    One stack to rule them all
  • 4
    Great load balancer
  • 1
    Amazon aws
  • 1
    Java syntax
Pros
  • 1
    SAP Replication server is clearly superior to MS SQL Se
  • 1
    HADR does not lose data is superior to Allwayson which
  • 1
    Max number of connection is 350000
  • 1
    HADR dont loose data
  • 1
    Replication server the best

What are some alternatives to Hadoop, Sybase?

MongoDB

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.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

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.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

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