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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?
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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 |
| アジャイル
How long does it take to complete the project?
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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 |
| 基幹業務に対応できるエンタープライズ仕様
Secure? Reliable? Proven?
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Yes
Proven reliability and advanced security for mission-critical environments |
Yes
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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?
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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 |
