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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. Apache Flink vs Atlas-DB

Apache Flink vs Atlas-DB

OverviewDecisionsComparisonAlternatives

Overview

Apache Flink
Apache Flink
Stacks534
Followers879
Votes38
GitHub Stars25.4K
Forks13.7K
Atlas-DB
Atlas-DB
Stacks6
Followers77
Votes0
GitHub Stars3.5K
Forks324

Apache Flink vs Atlas-DB: What are the differences?

Apache Flink and Atlas-DB are both popular tools in the data processing and management space, serving different purposes and catering to different use cases. Below are some key differences between Apache Flink and Atlas-DB.

1. **Processing Model**: Apache Flink is a stream processing framework that is designed for real-time processing of large data streams with low latency. On the other hand, Atlas-DB is a distributed key-value store that focuses on providing strong consistency guarantees for transactional data.

2. **Use Case**: Apache Flink is commonly used for real-time analytics, event-driven applications, and stream processing tasks where low latency and high throughput are critical. In contrast, Atlas-DB is more suited for applications requiring strong consistency and ACID transactions in a distributed environment.

3. **Programming Language Support**: Apache Flink supports various programming languages such as Java, Scala, and Python, making it versatile for different use cases. Atlas-DB, on the other hand, is primarily used with Java applications and does not have the same level of language support as Flink.

4. **Architecture**: Apache Flink has a distributed architecture with support for master-slave and peer-to-peer communication patterns, enabling fault tolerance and high availability. In comparison, Atlas-DB architecture is designed around the concept of tablets, which are distributed across multiple servers for data storage and retrieval.

5. **Data Model**: Apache Flink operates on streams of data, processing them in real-time with support for event-driven processing and windowing operations. Atlas-DB, on the other hand, operates on key-value pairs, enabling efficient storage and retrieval of structured data with strong consistency guarantees.

6. **Community and Ecosystem**: Apache Flink has a vibrant open-source community with a wide range of libraries, connectors, and tools available for developers to leverage. Atlas-DB, while open-source, has a smaller community and ecosystem compared to Flink, limiting the availability of third-party integrations and extensions. 

In Summary, Apache Flink is suited for real-time stream processing tasks with low latency requirements, while Atlas-DB is optimal for applications that demand strong consistency and transactional support in a distributed key-value store.

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Advice on Apache Flink, Atlas-DB

Nilesh
Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceonElasticsearchElasticsearchKafkaKafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

576k views576k
Comments

Detailed Comparison

Apache Flink
Apache Flink
Atlas-DB
Atlas-DB

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

Atlas was developed by Netflix to manage dimensional time series data for near real-time operational insight. Atlas features in-memory data storage, allowing it to gather and report very large numbers of metrics, very quickly.

Hybrid batch/streaming runtime that supports batch processing and data streaming programs.;Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.;Flexible and expressive windowing semantics for data stream programs;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance
Manages dimensional time series data; In-memory data storage; Captures operational intelligence
Statistics
GitHub Stars
25.4K
GitHub Stars
3.5K
GitHub Forks
13.7K
GitHub Forks
324
Stacks
534
Stacks
6
Followers
879
Followers
77
Votes
38
Votes
0
Pros & Cons
Pros
  • 16
    Unified batch and stream processing
  • 8
    Out-of-the box connector to kinesis,s3,hdfs
  • 8
    Easy to use streaming apis
  • 4
    Open Source
  • 2
    Low latency
No community feedback yet
Integrations
YARN Hadoop
YARN Hadoop
Hadoop
Hadoop
HBase
HBase
Kafka
Kafka
No integrations available

What are some alternatives to Apache Flink, Atlas-DB?

dbForge Studio for MySQL

dbForge Studio for MySQL

It is the universal MySQL and MariaDB client for database management, administration and development. With the help of this intelligent MySQL client the work with data and code has become easier and more convenient. This tool provides utilities to compare, synchronize, and backup MySQL databases with scheduling, and gives possibility to analyze and report MySQL tables data.

dbForge Studio for Oracle

dbForge Studio for Oracle

It is a powerful integrated development environment (IDE) which helps Oracle SQL developers to increase PL/SQL coding speed, provides versatile data editing tools for managing in-database and external data.

dbForge Studio for PostgreSQL

dbForge Studio for PostgreSQL

It is a GUI tool for database development and management. The IDE for PostgreSQL allows users to create, develop, and execute queries, edit and adjust the code to their requirements in a convenient and user-friendly interface.

dbForge Studio for SQL Server

dbForge Studio for SQL Server

It is a powerful IDE for SQL Server management, administration, development, data reporting and analysis. The tool will help SQL developers to manage databases, version-control database changes in popular source control systems, speed up routine tasks, as well, as to make complex database changes.

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Liquibase

Liquibase

Liquibase is th leading open-source tool for database schema change management. Liquibase helps teams track, version, and deploy database schema and logic changes so they can automate their database code process with their app code process.

Sequel Pro

Sequel Pro

Sequel Pro is a fast, easy-to-use Mac database management application for working with MySQL databases.

DBeaver

DBeaver

It is a free multi-platform database tool for developers, SQL programmers, database administrators and analysts. Supports all popular databases: MySQL, PostgreSQL, SQLite, Oracle, DB2, SQL Server, Sybase, Teradata, MongoDB, Cassandra, Redis, etc.

Presto

Presto

Distributed SQL Query Engine for Big Data

dbForge SQL Complete

dbForge SQL Complete

It is an IntelliSense add-in for SQL Server Management Studio, designed to provide the fastest T-SQL query typing ever possible.

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