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Database Comparisons vs Marklogic

Our goal with these comparisons is to provide a framework for how to think about comparing data management solutions and also highlight how MarkLogic products fit into your existing architecture. There is no one perfect solution for all use cases and it’s important to think in terms of trade-offs and how your architecture will evolve over time.

Key Comparisons

To start, we picked some of the key comparisons that frequently come up when we talk to customers. As you read the comparisons, consider the following key question: “What’s your business goal?”. By starting there and not with a preconceived list of features to compare against, we think you will develop better requirements that are more oriented toward solving both your data problems and your business problems.

How Does MarkLogic Data Hub Compare?

The below table provides a high-level overview of how MarkLogic Data Hub compares. Because it unifies many technologies into one data platform, our customers often compare it to the combination of other technologies that would be required to achieve similar functionality. And, it is not either/or — many customers use a data hub alongside other technologies. The central question as you consider your overall architecture is whether it is getting simpler or more complex?

OverviewData hub providing flexible data integration, management, and search for all enterprise data. Powered by MarkLogic ServerTraditional Enterprise Data Warehouse (EDW) such as Oracle integrated with a traditional ETL tool like InformaticaCustom-built cloud data hub architecture using managed cloud service components from a large cloud providerData lake using Hadoop and various data model-specific databases, a search engine, and an ETL tool
Does it handle transactions and analytics? Is it multi-model?Proven resultsNot for OLTP. Not truly multi-model. Non-relational data is a poor fit (slow, expensive)Every component must be individually deployed, integrated, monitored, secured, and paid forPatchwork architecture optimized for Data Scientists. Similar problems with needing to manage and integrate each tool separately
AgileYesNot AgileMaybeNo
How long does it take to complete the project?10x faster at integrating data than alternativesLong ETL timelines, everything must be modeled upfrontOnly quick for small projects. Exponentially more complex for large projectsLong implementation schedules, even for data science work
Enterprise ReadyYesNot AgileGetting ThereNo
Secure? Reliable? Proven?Proven reliability and advanced security for mission-critical environmentsYesIndividual components are secure. Problems arise when they are integratedCannot be governed at scale

How Does MarkLogic Server Compare?

The below table provides a summary of how MarkLogic Server—our multi-model database—compares directly with other popular database technologies according to their ability to power a data hub architecture.

MarkLogic Server Oracle DynamoDB MongoDB
Proven multi-model flexibility
Supports industry standard non-relational types, but not adequately
AWS has a different DBMS per data model
Document only, stores documents as non-standard BSON
Certified security controls and proven reputation
Limited granularity and lineage tracking. Pushes responsibility on developers
Limited granularity and lineage tracking. Pushes responsibility on developers
Distributed TransactionsYes
ACID transactions proven at scale. All ANSI levels supported
Simple ACID transactions, not proven
MongoDB 4.2.6 failed independent tests for ACID compliance, showing read skew, cyclic information flow, duplicate writes, and internal consistency violations
Proven scalability and elasticity with superior performance to price ratio
Scaling Oracle often requires forking data to new silo. Scale-out is expensive
Difficult but less expensive than relational, often requires downtime and refactoring
Cloud NeutralYes
Proven cloud neutrality
Though may require re-licensing
Only runs in AWS
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