Advanced Sqoop integrations often involve Apache Hive for optimized data transfers and transformations. Hive provides a data warehousing infrastructure on top of Hadoop, allowing for SQL-like queries and efficient data processing.

Partitioning in Apache Hive is significant for improved query performance. By partitioning data based on certain columns, Hive can skip unnecessary data scans during query execution, resulting in faster query performance and reduced resource consumption.

Heap dumps in Hadoop applications can be utilized for analyzing memory issues. By capturing and analyzing heap dumps, developers can identify memory leaks, inefficient memory usage, and other memory-related issues, facilitating advanced debugging and optimization of the application's memory footprint.

To handle large-scale data processing, Hadoop clusters are often scaled 'Horizontally.' This means adding more commodity hardware or nodes to the existing cluster, allowing it to distribute the workload and handle increased data processing demands.

For time-series data storage, configuring HBase with Time-to-Live (TTL) can be advantageous. TTL allows you to automatically expire data after a specified period, which is useful for managing and cleaning up older time-series data, optimizing storage, and improving query performance.

In a Hadoop cluster, the YARN (Yet Another Resource Negotiator) tool is used for cluster resource management and job scheduling. YARN separates resource management and job scheduling functionalities in Hadoop, allowing for more efficient cluster utilization.

In a Hadoop-based ETL process, choosing ORC (Optimized Row Columnar) file format with Gzip compression is ideal for optimized data transfer. ORC provides efficient storage and Gzip offers a good balance between compression ratio and speed.

The use of DataFrames in Apache Spark significantly enhances the speed of data transformations in a distributed environment. DataFrames provide a higher-level abstraction and optimization opportunities for Spark's Catalyst query engine, making it more efficient for processing large-scale data.

The high-availability feature in YARN is achieved through ResourceManager Failover. This ensures continuous operation by having a standby ResourceManager ready to take over in case the primary ResourceManager fails, minimizing downtime and maintaining cluster availability.

In the event of a NameNode crash, the typical recovery procedure involves restoring from the Secondary NameNode. The Secondary NameNode contains a checkpoint of the metadata, allowing for a faster recovery compared to restarting the entire cluster.

Apache Pig scripts are primarily written in Pig Latin, a high-level scripting language designed for expressing data analysis programs in a concise and readable way. Pig Latin scripts are then translated into MapReduce jobs for execution on a Hadoop cluster.

The process of ETL (Extract, Transform, Load) is crucial for transferring bulk data between Hadoop and external data sources. ETL involves extracting data from external sources, transforming it into a suitable format, and loading it into the Hadoop cluster for analysis.