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WorksheetsHigh-throughput streaming with Bigtable
Total questions: 27
Worksheet time: 14mins
What is Bigtable particularly suited for?
Low-throughput applications
Highly structured data
Real-time lookup capacity with high throughput
Small data volumes less than 1 TB
What is a limitation of BigQuery mentioned in the script?
High latency
Limited storage capacity
Lack of SQL support
Inability to handle structured data
What is the primary index used in Bigtable?
Column index
Row Key
Timestamp index
Metadata index
How does Bigtable handle "hot spots" of activity?
It deletes the overactive Tablet
It splits the Tablet in two
It moves the Tablet to another cluster
It compresses the Tablet
What is a common use case for Bigtable mentioned in the script?
Processing small transactional data
Running SQL queries and joins
Storing machine learning model training data
Handling time-series data such as CPU usage
How does Bigtable store data?
In relational databases
In tables with rows and columns
In unstructured text files
In blockchain format
What design principle does Bigtable use for speed?
Complex indexing
Multiple secondary indexes
Reduced Instruction Set Computing (RISC)
Hierarchical data structures
What is a constructed Row Key used for in Bigtable?
To add multiple indexes
To avoid scanning the entire table
To enable SQL-like joins
To increase data redundancy
How does Bigtable ensure efficient data retrieval from Column Families?
By compressing all data
By using multiple secondary indexes
By grouping related rows into families
By sorting all rows alphabetically
What happens when data is deleted in Bigtable?
It is immediately removed
It is marked for deletion and skipped during processing
It is archived in a separate database
It triggers a table reorganization immediately
BigQuery is generally a good solution, but what is a situation where it may not be sufficient?
High latency requirements
Low latency requirements
High cost
Limited data storage
Which Google Cloud service is best suited for applications needing very high throughput and scalability?
BigQuery
Looker Studio
Bigtable
Dataproc
What must you know a lot about to use Bigtable effectively?
The schema structure
The data and how it will be queried
The cost of operations
The network latency
Which type of data organization makes compression more efficient in Bigtable?
Randomly distributed data
Data with identical values near each other
Data organized by timestamps
Data organized by hash values
What is the Row Key designed for in Bigtable?
To optimize for data compression
To enhance data redundancy
To enable fast access through lexicographic organization
To support multiple secondary indexes
What does Bigtable use to store actual data elements?
Rows and columns
Tablets
Metadata
B-trees
Bigtable stores data in a file system called what?
HDFS
BigQuery
Datastore
Colossus
For which of the following is Bigtable NOT well suited?
High-throughput applications
Highly structured data
Machine learning algorithms
IoT data
What is the primary advantage of using constructed row keys in Bigtable?
They allow for multiple secondary indexes.
They enable efficient query performance through minimal sorting and searching.
They provide built-in SQL query support.
They increase the compression efficiency of data.
Which of the following scenarios would NOT be a good fit for using Bigtable?
High-throughput, real-time lookups for an application.
Time-series data such as CPU and memory usage over time.
Highly structured transactional data requiring SQL queries.
Storing machine learning algorithm training data.
How does Bigtable handle data deletion to maintain read and write efficiency?
It immediately removes the row from the table.
It marks the row for deletion and periodically compacts the table.
It archives the row to a secondary storage system.
It overwrites the row with null values.
What is a significant benefit of using column families in Bigtable?
They allow for SQL-like joins between tables.
They enable more efficient data retrieval without pulling all data from the row.
They increase the maximum row size to over 100 MB.
They automatically compress data to save storage space.
In what way does Bigtable ensure high availability and quick recovery of data?
By replicating data across multiple nodes in the cluster.
By storing metadata about tablets on the VMs in the Bigtable cluster.
By using a default configuration of 3 replicas for data durability.
By maintaining secondary indexes for each table.
How does reversing timestamps in the row key design improve query performance?
It helps in sorting data lexicographically.
It allows for the most recent events to appear at the start of the table.
It reduces the need for column families.
It enhances data compression efficiency.
Which of the following can help improve performance of Bigtable?
(Select all 3 correct responses)
Add more nodes
Change schema to minimize data skew
Use HDD instead of SDD
Clients and Bigtable are in same zone
Which of the following are true about Bigtable?
(Mark all 3 correct responses)
Offers very low-latency in the order of milliseconds
Ideal for >1TB data
Great for time-series data
Support for SQL
Bigtable learns access patterns and attempts to distribute reads and storage across nodes evenly
True
False
