Mistake #4: Treating a Hadoop Data Lake like a normal database
Treating the Hadoop data lake like a normal database is a typical misunderstanding. Hadoop is powerful, but not structured in the same way as, for example, a database from Oracle, HP Vertica or Teradata. Comparing ELT with a classic staging area tempts you to mix things up. The data lake metaphor also tempts you to imagine the data lake as a clear mountain lake in which everything can be found quickly. In reality, however, the data lake often degenerates into a swamp. Here it is particularly important to pay attention to the collection and use of metadata and the creation of a comprehensible structure right from the first step - loading the raw data. A data lake typically complements an existing data warehouse (DWH) and enables you to efficiently process data that previously did not quite fit into the "SQL corset".
For most companies, security is a sensitive issue and should therefore be taken into account right from the start of planning. It is often the benin telegram screening case that initial prototypes are set up without any security concept, so that the technology chosen later does not (yet) offer the required security. To change this, the following security features should be included in the plan:
Authentication: controls who has access to the cluster.
Authorization: controls what individual users in the cluster are allowed to do (with the data).
Audit and tracking: Tracks and records all user actions for documentation purposes.
Data protection regulations: Use of standard methods for data encryption in accordance with the applicable data protection guidelines. This also includes anonymization and tokenization.
Automation: Preparing, linking, reporting and sending alerts based on Hadoop data.
Predictive Analytics: Integration of predictive analytics to analyze data and user behavior for anomalies in near real-time.