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Citus vs Hadoop: What are the differences?
Introduction
In the world of big data processing, Citus and Hadoop are two popular solutions that offer distributed computing capabilities for handling large volumes of data. However, there are key differences between these two technologies. In this article, we will explore and compare those differences.
Architecture: The fundamental difference lies in their architecture design. Hadoop follows the distributed file system (HDFS) model, where data is stored in a distributed manner across multiple nodes, and processing is performed by MapReduce jobs. On the other hand, Citus is an extension to the Postgres database that distributes and parallelizes data across multiple nodes, enabling distributed query processing.
Data Processing Paradigm: While Hadoop is primarily designed for batch processing of data, Citus provides real-time performance by leveraging the massively parallel processing (MPP) capabilities of Postgres. Citus allows for concurrent reads and writes, making it suitable for online transaction processing (OLTP) workloads.
Query Language Support: Hadoop uses its own query language called HiveQL, which is based on SQL but has some variations. In contrast, Citus leverages the full power of SQL as it is built as an extension to Postgres. This allows users to leverage their existing SQL skills and tools while working with Citus.
Data Storage: Hadoop utilizes a distributed file system where data is stored across multiple nodes. Data is typically stored in a schemaless format, such as Hadoop Distributed File System (HDFS) or Apache Parquet. In Citus, data is stored in a traditional relational database format, following the table-based structure of Postgres.
Ease of Deployment and Administration: Hadoop clusters require complex setup and configuration, involving various components like HDFS, YARN, and MapReduce. Additionally, Hadoop clusters often involve managing multiple specialized machines. In contrast, Citus can be easily deployed as an extension to an existing Postgres database, reducing the need for separate cluster management and simplifying administration.
Maturity and Ecosystem: Hadoop has been around for a longer time and has a more mature ecosystem with a wide range of tools and technologies built around it, such as Hive, Pig, and Spark. Citus, being an extension to Postgres, benefits from the extensive ecosystem and tooling that exists for Postgres, including various SQL extensions, connectors, and integration options.
In summary, Citus and Hadoop differ in their architecture, data processing paradigms, query language support, data storage models, ease of deployment, administration, and the maturity of their ecosystems. These differences allow organizations to choose the right technology based on their specific requirements and use cases.
For a property and casualty insurance company, we currently use MarkLogic and Hadoop for our raw data lake. Trying to figure out how snowflake fits in the picture. Does anybody have some good suggestions/best practices for when to use and what data to store in Mark logic versus Snowflake versus a hadoop or all three of these platforms redundant with one another?
for property and casualty insurance company we current Use marklogic and Hadoop for our raw data lake. Trying to figure out how snowflake fits in the picture. Does anybody have some good suggestions/best practices for when to use and what data to store in Mark logic versus snowflake versus a hadoop or all three of these platforms redundant with one another?
As i see it, you can use Snowflake as your data warehouse and marklogic as a data lake. You can add all your raw data to ML and curate it to a company data model to then supply this to Snowflake. You could try to implement the dw functionality on marklogic but it will just cost you alot of time. If you are using Aws version of Snowflake you can use ML spark connector to access the data. As an extra you can use the ML also as an Operational report system if you join it with a Reporting tool lie PowerBi. With extra apis you can also provide data to other systems with ML as source.
I have a lot of data that's currently sitting in a MariaDB database, a lot of tables that weigh 200gb with indexes. Most of the large tables have a date column which is always filtered, but there are usually 4-6 additional columns that are filtered and used for statistics. I'm trying to figure out the best tool for storing and analyzing large amounts of data. Preferably self-hosted or a cheap solution. The current problem I'm running into is speed. Even with pretty good indexes, if I'm trying to load a large dataset, it's pretty slow.
Druid Could be an amazing solution for your use case, My understanding, and the assumption is you are looking to export your data from MariaDB for Analytical workload. It can be used for time series database as well as a data warehouse and can be scaled horizontally once your data increases. It's pretty easy to set up on any environment (Cloud, Kubernetes, or Self-hosted nix system). Some important features which make it a perfect solution for your use case. 1. It can do streaming ingestion (Kafka, Kinesis) as well as batch ingestion (Files from Local & Cloud Storage or Databases like MySQL, Postgres). In your case MariaDB (which has the same drivers to MySQL) 2. Columnar Database, So you can query just the fields which are required, and that runs your query faster automatically. 3. Druid intelligently partitions data based on time and time-based queries are significantly faster than traditional databases. 4. Scale up or down by just adding or removing servers, and Druid automatically rebalances. Fault-tolerant architecture routes around server failures 5. Gives ana amazing centralized UI to manage data sources, query, tasks.
Pros of Citus
- Multi-core Parallel Processing6
- Drop-in PostgreSQL replacement3
- Distributed with Auto-Sharding2
Pros of Hadoop
- Great ecosystem39
- One stack to rule them all11
- Great load balancer4
- Amazon aws1
- Java syntax1
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