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既に「Data Hub QuickStart チュートリアル – ビジネス課題の解決から考えるデータ統合の進め方 ＜前編＞」を実施されている方は、本チュートリアルを進める前に下記を実行して再度データの取り込みをお願いいたします。前回のサンプルデータに一部問題があったため修正しております。

※ 1. QuickStartメニューのDashboardをクリックし、Databasesの横のドクロマークをクリック。”Do you really want to remove all files from all of your Data Hub Databases?”と出てくるのでCLEAR!をクリックし、データを全部削除する
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Don’t be offended by the play on words. Graph databases are very powerful and not generally involved in bribery. In fact, given its ability to discover fraudulent activity through the relationships it captures, a graph database is quite good at uncovering transgressions such as payoffs and other forms of corruption.

With that caveat aside, let’s explore why a graph database should not be the ONLY data-management technology for capturing various 360 views—especially for customers—in the

This is the third in a series of blogs for people coming from a relational background, to help you understand the differences in how MarkLogic handles data integration and access.

TL/DR: 

• Because MarkLogic easily manages complex, diverse and changing data structures, you can take an iterative, agile development approach versus a waterfall approach—modeling and harmonizing data as needed for each project, versus doing upfront data modeling and ETL.

This is the second in a series of blogs for people coming from a relational background, to help you understand the differences in how MarkLogic handles data integration and access.

TL/DR: 

• Data modeling in MarkLogic is an iterative process that focuses on meeting specific project goals for user data access, versus the relational approach that requires significant upfront data modeling and ETL.
• With MarkLogic, best practice is to maintain data as