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How do MarkLogic Products Compare?

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 Service Compare?

The below table provides a high-level overview of how MarkLogic Data Hub Service 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?

MarkLogic Data Hub-Service Data Warehouse + ETL Managed Cloud Services Data Lake + Open Source Components
Cloud data hub providing flexible data integration, management, and search for all enterprise data. Powered by MarkLogic Server. Traditional Enterprise Data Warehouse (EDW) such as Oracle integrated with a traditional ETL tool like Informatica Custom-built cloud data hub architecture using managed cloud service components from a large cloud provider.

For example, AWS provides DynamoDB (documents), NeptuneDB (graph), Elasticsearch Service (search), Amazon S3 (object storage), Glue (ETL), and Athena (query service)

Data lake using Hadoop and various data model-specific databases, a search engine, and an ETL tool.

Many variations exist, but one example might include Cloudera with MongoDB (documents), Lucene (search), Neo4j (graph), and Talend (ETL)

Unified

Does it handle transactions and analytics? Is it multi-model?

Yes

Proven results

Maybe

Not for OLTP. Not truly multi-model. Non-relational data is a poor fit (slow, expensive)

No

Every component must be individually deployed, integrated, monitored, secured, and paid for

No

Patchwork architecture optimized for Data Scientists. Similar problems with needing to manage and integrate each tool separately

Flexibel

How long does it take to complete the project?

Yes

10x faster at integrating data than alternatives

Not Agile

Long ETL timelines, everything must be modeled upfront

Maybe

Only quick for small projects. Exponentially more complex for large projects

No

Long implementation schedules, even for data science work

Für den Einsatz in Unternehmen

Secure? Reliable? Proven?

Yes

Proven reliability and advanced security for mission-critical environments

Yes

Getting There

Individual components are secure. Problems arise when they are integrated

No

Cannot be governed at scale

Cloud Native

Is it fully managed? Which clouds?

Yes

Cloud native, multi-cloud

Maybe

Complex cloud deployments. May not be a true SaaS offering

Not cloud neutral

Cloud native, but not cloud neutral

Complex

Management is complex and costly

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
Multi-Modell

Yes

Proven multi-model flexibility

Maybe

Supports industry standard non-relational types, but not adequately

No

AWS has a different DBMS per data model

No

Document only, stores documents as non-standard BSON

Sicherheit

Yes

Certified security controls and proven reputation

Yes

Maybe

Limited granularity and lineage tracking. Pushes responsibility on developers

Maybe

Limited granularity and lineage tracking. Pushes responsibility on developers

Distributed Transactions

Yes

ACID transactions proven at scale. All ANSI levels supported

Yes

Maybe

Simple ACID transactions, not proven

No

MongoDB 4.2.6 failed independent tests for ACID compliance, showing read skew, cyclic information flow, duplicate writes, and internal consistency violations

Skalierbarkeit

Yes

Proven scalability and elasticity with superior performance to price ratio

Maybe

Scaling Oracle often requires forking data to new silo. Scale-out is expensive

Yes

Maybe

Difficult but less expensive than relational, often requires downtime and refactoring

Cloud Neutral

Yes

Proven cloud neutrality

Yes

Though may require re-licensing

No

Only runs in AWS

Yes

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