Questions et reponses d'entretien

Example:

In a relational database, a schema might include tables for 'users' and 'orders,' with defined fields for each.

Example:

Selecting all customers and their orders, even if some customers have not placed any orders (left join).

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In a retail system, a database may store customer orders, while a data warehouse aggregates sales data for business intelligence.

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Extracting customer data from a CRM system, transforming it into a standardized format, and loading it into a data warehouse.

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Replacing missing age values in a dataset with the mean age of the available data.

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A data pipeline that extracts data from log files, transforms it into a structured format, and loads it into a data warehouse.

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Creating an index on the 'user_id' column to quickly locate user information in a large user table.

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Batch processing might involve processing daily sales data, while stream processing handles real-time sensor data.

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Implementing encryption for sensitive customer information stored in a database.

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Breaking down a large customer table into smaller tables like 'customers' and 'orders' to avoid repeating customer information for each order.

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In a star schema, a sales fact table is linked to dimension tables like 'time' and 'product.' In a snowflake schema, the 'time' dimension may be further normalized into 'year,' 'quarter,' and 'month' tables.

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Rewriting a slow query by adding an index on the columns used in the WHERE clause.

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Documenting the data lineage of a customer information data pipeline, showing the extraction, transformation, and loading processes.

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Using Apache Spark to process large-scale log data and extract meaningful insights in near real-time.

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Identifying and eliminating duplicate customer records in a CRM database.

Example:

Using a NoSQL database to store and retrieve JSON documents in a web application.

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Using a data catalog to search for and understand the metadata of a specific dataset within an organization.

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Ensuring that a financial transaction is atomic (either fully completed or fully rolled back) to maintain data integrity.

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Applying columnar compression to a large dataset in a data warehouse to reduce storage costs.

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Identifying and addressing data skewness issues in a Spark job to improve overall processing time.

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Storing and querying large volumes of historical sales data more efficiently using columnar storage.

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Implementing data governance policies to ensure that sensitive customer information is handled securely and in compliance with regulations.

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Building a scalable data pipeline using Apache Hadoop and Apache Spark to process large volumes of log data.

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Adding new fields to a data warehouse table to accommodate additional attributes without affecting existing queries.

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Implementing a real-time fraud detection system using data streaming to analyze transaction data as it occurs.

Example:

Horizontally partitioning a customer table based on regions, and vertically partitioning it based on customer information and order information.

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Partitioning a table based on date, so each partition contains data for a specific time range.

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Choosing between consistency and availability in a distributed database during a network partition.

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Sharding a user database based on geographic regions to distribute the load and enhance query performance.

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Re-partitioning a dataset based on a different key to distribute the data more evenly in a Spark job.