MarkLogic vs RocksDB

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MarkLogic

32
52
+ 1
26
RocksDB

89
208
+ 1
11
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MarkLogic vs RocksDB: What are the differences?

What is MarkLogic? Schema-agnostic Enterprise NoSQL database technology, coupled w/ powerful search & flexible application services. MarkLogic is the only Enterprise NoSQL database, bringing all the features you need into one unified system: a document-centric, schema-agnostic, structure-aware, clustered, transactional, secure, database server with built-in search and a full suite of application services.

What is RocksDB? Embeddable persistent key-value store for fast storage, developed and maintained by Facebook Database Engineering Team. RocksDB is an embeddable persistent key-value store for fast storage. RocksDB can also be the foundation for a client-server database but our current focus is on embedded workloads. RocksDB builds on LevelDB to be scalable to run on servers with many CPU cores, to efficiently use fast storage, to support IO-bound, in-memory and write-once workloads, and to be flexible to allow for innovation.

MarkLogic and RocksDB can be categorized as "Databases" tools.

Some of the features offered by MarkLogic are:

  • Search and Query
  • ACID Transactions
  • High Availability and Disaster Recovery

On the other hand, RocksDB provides the following key features:

  • Designed for application servers wanting to store up to a few terabytes of data on locally attached Flash drives or in RAM
  • Optimized for storing small to medium size key-values on fast storage -- flash devices or in-memory
  • Scales linearly with number of CPUs so that it works well on ARM processors

"RDF Triples" is the top reason why over 3 developers like MarkLogic, while over 2 developers mention "Very fast" as the leading cause for choosing RocksDB.

RocksDB is an open source tool with 14.3K GitHub stars and 3.12K GitHub forks. Here's a link to RocksDB's open source repository on GitHub.

Advice on MarkLogic and RocksDB
Needs advice
on
SnowflakeSnowflakeMarkLogicMarkLogic
and
HadoopHadoop

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?

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Needs advice
on
SnowflakeSnowflakeMarkLogicMarkLogic
and
HadoopHadoop

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?

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Replies (1)
Ivo Dinis Rodrigues
none of you bussines at Marklogic · | 1 upvotes · 4.8K views
Recommends

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.

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Pros of MarkLogic
Pros of RocksDB
  • 5
    RDF Triples
  • 3
    JavaScript
  • 3
    Enterprise
  • 3
    Marklogic is absolutely stable and very fast
  • 3
    REST API
  • 3
    JSON
  • 2
    Semantics
  • 2
    Multi-model DB
  • 1
    Bitemporal
  • 1
    Tiered Storage
  • 5
    Very fast
  • 3
    Made by Facebook
  • 2
    Consistent performance
  • 1
    Ability to add logic to the database layer where needed

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- No public GitHub repository available -

What is MarkLogic?

MarkLogic is the only Enterprise NoSQL database, bringing all the features you need into one unified system: a document-centric, schema-agnostic, structure-aware, clustered, transactional, secure, database server with built-in search and a full suite of application services.

What is RocksDB?

RocksDB is an embeddable persistent key-value store for fast storage. RocksDB can also be the foundation for a client-server database but our current focus is on embedded workloads. RocksDB builds on LevelDB to be scalable to run on servers with many CPU cores, to efficiently use fast storage, to support IO-bound, in-memory and write-once workloads, and to be flexible to allow for innovation.

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Jan 26 2022 at 4:34AM

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What are some alternatives to MarkLogic and RocksDB?
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.
Neo4j
Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.
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.
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.
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.
See all alternatives