Why HBase provides real-time access to big data in Hadoop - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an HBase table with a column family.

Hadoop

create 'my_table', '[1]'

Drag options to blanks, or click blank then click option'

A'cf1'

B'column1'

C'row1'

D'data'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a column name instead of a column family.

Omitting the quotes around the column family name.

HBase tables require at least one column family, which is specified when creating the table.

2fill in blank

medium

Complete the code to put data into an HBase table.

Hadoop

put 'my_table', 'row1', '[1]', 'value1'

Drag options to blanks, or click blank then click option'

A'row1'

B'my_table'

C'cf1:col1'

D'value1'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the row key or table name instead of the column identifier.

Forgetting the colon between column family and qualifier.

Data is inserted into HBase by specifying the row key, column family and qualifier, and the value.

3fill in blank

hard

Fix the error in the code to get data from HBase.

Hadoop

get 'my_table', '[1]'

Drag options to blanks, or click blank then click option'

A'row1'

B'value1'

C'my_table'

D'cf1:col1'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the column identifier instead of the row key.

Using the table name instead of the row key.

The get command requires the row key to retrieve data, not the column or table name.

4fill in blank

hard

Fill both blanks to scan an HBase table and filter rows with a specific prefix.

Hadoop

scan 'my_table', [1] => [2]('row')

Drag options to blanks, or click blank then click option'

A{FILTER

BPrefixFilter

CRowFilter

DValueFilter

Attempts:

3 left

💡 Hint

Common Mistakes

Using RowFilter or ValueFilter instead of PrefixFilter.

Omitting the FILTER keyword or curly braces.

The scan command uses a FILTER with a PrefixFilter to select rows starting with a prefix.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps row keys to values for rows with values greater than 10.

Hadoop

result = [1]: [2] for k, v in data.items() if v [3] 10

Drag options to blanks, or click blank then click option'

Ak

Bv

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong comparison operator.

Swapping keys and values in the comprehension.

The comprehension maps keys to values where the value is greater than 10.