Overview

Microsoft SQL Server

Stacks21.3K

Followers15.5K

Votes540

MarkLogic

Stacks43

Followers71

Votes26

MarkLogic vs Microsoft SQL Server: What are the differences?

Scalability: MarkLogic is designed for handling large volumes of semi-structured and unstructured data in a highly scalable manner, making it suitable for big data applications. In contrast, Microsoft SQL Server is more suited for handling structured data in traditional relational databases, with scalability limitations dependent on the hardware and configuration of servers.
Data Model: MarkLogic uses a flexible document data model, allowing for the storage and retrieval of JSON, XML, RDF, and other types of documents easily. On the other hand, Microsoft SQL Server follows a rigid relational data model with tables, rows, and columns, making data modeling less flexible compared to MarkLogic.
Search Capabilities: MarkLogic offers robust search functionalities with built-in indexing, faceted search, and other advanced search features to efficiently query and retrieve data across a vast repository. Microsoft SQL Server lacks advanced search capabilities out-of-the-box and may require additional tools or configurations to achieve similar search functionalities.
Multi-Model Support: MarkLogic supports multiple data models within a single platform, enabling users to work with various data formats seamlessly. In contrast, Microsoft SQL Server primarily focuses on the relational data model and does not provide native support for other data models like JSON or XML without additional plugins or extensions.
Data Security: MarkLogic emphasizes security with features like element-level security, encryption, and fine-grained access controls to ensure data protection and compliance with regulations. While Microsoft SQL Server also offers robust security features, it may require additional configurations or third-party tools to achieve the same level of data security as MarkLogic.
Developer Productivity: MarkLogic provides a rich set of APIs, libraries, and tools that streamline development tasks and simplify the integration of diverse data sources. In comparison, Microsoft SQL Server offers a comprehensive set of development tools but may require more intricate coding and configurations for handling complex data structures and operations.

In Summary, MarkLogic and Microsoft SQL Server differ in terms of scalability, data model, search capabilities, multi-model support, data security, and developer productivity.

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Advice on Microsoft SQL Server, MarkLogic

Erin

IT Specialist

Mar 10, 2020

Needs adviceon

Microsoft SQL Server

MySQL

PostgreSQL

I am a Microsoft SQL Server programmer who is a bit out of practice. I have been asked to assist on a new project. The overall purpose is to organize a large number of recordings so that they can be searched. I have an enormous music library but my songs are several hours long. I need to include things like time, date and location of the recording. I don't have a problem with the general database design. I have two primary questions:

I need to use either @{MySQL}|tool:1025| or @{PostgreSQL}|tool:1028| on a @{Linux}|tool:10483| based OS. Which would be better for this application?
I have not dealt with a sound based data type before. How do I store that and put it in a table? Thank you.

668k views668k

Comments

Mr

SVP CTO

Apr 22, 2021

Needs adviceon

MarkLogic

Hadoop

Snowflake

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?

136k views136k

Comments

Mr

SVP CTO

Apr 22, 2021

Needs advice

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?

23.6k views23.6k

Comments

Detailed Comparison

Microsoft SQL Server	MarkLogic
Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.	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.
-	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 21.3K	Stacks 43
Followers 15.5K	Followers 71
Votes 540	Votes 26
Pros & Cons
Pros 139 Reliable and easy to use 101 High performance 95 Great with .net 65 Works well with .net 56 Easy to maintain Cons 4 Expensive Licensing 2 Microsoft 1 Replication can loose the data 1 Allwayon can loose data in asycronious mode 1 Data pages is only 8k	Pros 5 RDF Triples 3 JSON 3 Enterprise 3 JavaScript 3 Marklogic is absolutely stable and very fast

What are some alternatives to Microsoft SQL Server, 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.

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.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.