Amazon DynamoDB vs MarkLogic

Overview

Amazon DynamoDB

Stacks4.0K

Followers3.2K

Votes195

MarkLogic

Stacks43

Followers71

Votes26

Amazon DynamoDB vs MarkLogic: What are the differences?

Introduction

In the comparison between Amazon DynamoDB and MarkLogic, there are several key differences to consider.

Data Model: Amazon DynamoDB is a NoSQL database service that uses a key-value and document data model, making it suitable for applications requiring high performance and scalability. On the other hand, MarkLogic is a NoSQL database that focuses on a multi-model approach, supporting document, triple, and graph data models in a single database. This versatility allows MarkLogic to handle a wider variety of data types and relationships.
Querying Capabilities: DynamoDB provides fast and efficient querying through its primary key, secondary indexes, and global secondary indexes. However, MarkLogic offers more advanced querying capabilities, including full-text search, range queries, geospatial queries, and semantic queries, making it ideal for complex data retrieval and analysis tasks.
ACID Compliance: Amazon DynamoDB guarantees high availability and durability through its strict consistency model, ensuring that data remains consistent even in the event of failures. MarkLogic also offers ACID compliance but provides more flexible consistency options, allowing users to choose between strong, eventual, and customizable consistency levels based on their application requirements.
Scalability and Performance: DynamoDB is designed for seamless scalability and can handle massive workloads with predictable performance. MarkLogic, on the other hand, is optimized for highly scalable deployments across clusters of servers, making it suitable for handling large datasets and demanding workloads with high performance.
Indexing: In DynamoDB, indexing is limited to primary key, local secondary index, and global secondary index. In contrast, MarkLogic offers flexible indexing options, including range indexes, geospatial indexes, and semantic indexes, allowing users to optimize query performance and enable advanced search functionalities in their applications.
Data Replication and Backup: DynamoDB provides automated data backups and multi-region replication to ensure data durability and availability. MarkLogic also offers robust data replication and backup features, allowing users to create disaster recovery plans and maintain data integrity across distributed environments efficiently.

In Summary, Amazon DynamoDB and MarkLogic differ in their data models, querying capabilities, ACID compliance, scalability and performance, indexing options, and data replication features.

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Advice on Amazon DynamoDB, MarkLogic

Doru

Solution Architect

Jun 9, 2019

Reviewon

Amazon DynamoDB

I use Amazon DynamoDB because it integrates seamlessly with other AWS SaaS solutions and if cost is the primary concern early on, then this will be a better choice when compared to AWS RDS or any other solution that requires the creation of a HA cluster of IaaS components that will cost money just for being there, the costs not being influenced primarily by usage.

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Detailed Comparison

Amazon DynamoDB	MarkLogic
With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.	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.
Automated Storage Scaling – There is no limit to the amount of data you can store in a DynamoDB table, and the service automatically allocates more storage, as you store more data using the DynamoDB write APIs;Provisioned Throughput – When creating a table, simply specify how much request capacity you require. DynamoDB allocates dedicated resources to your table to meet your performance requirements, and automatically partitions data over a sufficient number of servers to meet your request capacity;Fully Distributed, Shared Nothing Architecture	Search and Query;ACID Transactions;High Availability and Disaster Recovery;Replication;Government-grade Security;Scalability and Elasticity;On-premise or Cloud Deployment;Hadoop for Storage and Compute;Semantics;Faster Time-to-Results
Statistics
Stacks 4.0K	Stacks 43
Followers 3.2K	Followers 71
Votes 195	Votes 26
Pros & Cons
Pros 62 Predictable performance and cost 56 Scalable 35 Native JSON Support 21 AWS Free Tier 7 Fast Cons 4 Only sequential access for paginate data 1 Document Limit Size 1 Scaling	Pros 5 RDF Triples 3 JSON 3 REST API 3 Marklogic is absolutely stable and very fast 3 JavaScript
Integrations
Amazon RDS for PostgreSQL PostgreSQL MySQL SQLite Azure Database for MySQL	No integrations available

What are some alternatives to Amazon DynamoDB, MarkLogic?

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

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

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

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 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

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 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

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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Amazon DynamoDB vs MarkLogic: What are the differences?

Introduction

In the comparison between Amazon DynamoDB and MarkLogic, there are several key differences to consider.

Data Model: Amazon DynamoDB is a NoSQL database service that uses a key-value and document data model, making it suitable for applications requiring high performance and scalability. On the other hand, MarkLogic is a NoSQL database that focuses on a multi-model approach, supporting document, triple, and graph data models in a single database. This versatility allows MarkLogic to handle a wider variety of data types and relationships.
Querying Capabilities: DynamoDB provides fast and efficient querying through its primary key, secondary indexes, and global secondary indexes. However, MarkLogic offers more advanced querying capabilities, including full-text search, range queries, geospatial queries, and semantic queries, making it ideal for complex data retrieval and analysis tasks.
ACID Compliance: Amazon DynamoDB guarantees high availability and durability through its strict consistency model, ensuring that data remains consistent even in the event of failures. MarkLogic also offers ACID compliance but provides more flexible consistency options, allowing users to choose between strong, eventual, and customizable consistency levels based on their application requirements.
Scalability and Performance: DynamoDB is designed for seamless scalability and can handle massive workloads with predictable performance. MarkLogic, on the other hand, is optimized for highly scalable deployments across clusters of servers, making it suitable for handling large datasets and demanding workloads with high performance.
Indexing: In DynamoDB, indexing is limited to primary key, local secondary index, and global secondary index. In contrast, MarkLogic offers flexible indexing options, including range indexes, geospatial indexes, and semantic indexes, allowing users to optimize query performance and enable advanced search functionalities in their applications.
Data Replication and Backup: DynamoDB provides automated data backups and multi-region replication to ensure data durability and availability. MarkLogic also offers robust data replication and backup features, allowing users to create disaster recovery plans and maintain data integrity across distributed environments efficiently.

In Summary, Amazon DynamoDB and MarkLogic differ in their data models, querying capabilities, ACID compliance, scalability and performance, indexing options, and data replication features.