Essential

Refine your analytical toolkit. This track is a practical roadmap through the SQL, Spark, and Snowflake patterns used daily in industry. It locks in the technical core you need to handle data with confidence and focus on the actual analysis.

What You'll Master

96 questions strategically distributed across 4 essential Data Analysis domains:

SQL 33 questions

Spark 20 questions

Snowflake 22 questions

Python 24 questions

Pass Interviews At

Questions sourced from real interviews at top companies:

Google

Amazon

Microsoft

Questions in This Track

96 questions you'll work through:

1 Managing High I/O Processes Linux 2 Docker Multi-Architecture Image Docker 3 Average Order Value SQLAggregation 4 Join Employees and Departments SQLJoins 5 Filter Orders by Date Range SQLBasic 6 Find Customers Without Orders SQLJoins 7 Use COALESCE for Null Handling SQLData Manipulation 8 Merge Multiple Address Fields SQLData Manipulation 9 String Concatenation in SELECT SQLData Manipulation 10 Find Nth Highest Revenue SQLAggregation 11 Self-Join to Identify Missing Supervisors SQLJoins 12 Year-over-Year Revenue Growth SQLAggregation 13 Above Average Price Products SQLAdvanced 14 Calculate Cumulative Sales SQLAggregation 15 Find Overlapping Date Ranges SQLJoins 16 Set Operation: INTERSECT SQLJoins 17 Subquery for Best Order per Customer SQLJoins 18 Ranking with Dense_Rank SQLAggregation 19 Median Salary by Job Title SQLAggregation 20 String Splitting and Aggregation SQLAggregation 21 Salary Comparison with CTE Aggregation SQLAggregation 22 String Pattern Extraction in Descriptions SQLData Manipulation 23 Nested Subquery for Latest Record SQLJoins 24 Window Function for Moving Average SQLAggregation 25 Re-enrollment Rate Calculator SQLJoins 26 String Pattern Matching Using LIKE SQLJoins 27 Merge Employee and Department Records SQLAdvanced 28 Sequence Products by Price SQLAdvanced 29 Combine Data from Multiple Sources into Unified Report ProgrammingPython 30 Export SQLite Database to Parquet Format with Metadata ProgrammingPython 31 Top Categories by Average Price SQLAggregation 32 Customer Order Aggregation SQLJoins 33 Filter Popular Videos on a Streaming Platform SnowflakeFiltering 34 Replace Keywords in Social Media Post Text SnowflakeString Replacement 35 Filter Movies with Missing Box Office Data SnowflakeNull Handling 36 Daily Category Sales SnowflakeJoins 37 Filter and Uppercase Artifacts SnowflakeString Transformation 38 Combine Customer Orders and Products SnowflakeJoins 39 Anonymize User PII Data for a Social Media Platform SnowflakeString Functions 40 Product Sales and Inventory Data SnowflakeAggregation 41 Products and Duplicates SnowflakeDeduplication 42 Mortgage Rate Calculator SnowflakeJoins 43 Weekend Order Detection SnowflakeDatetime Operations 44 Flooring Company Data SnowflakeJoins 45 Rank Top Products by Revenue per Category SnowflakeWindows Functions 46 Highest SEO Score Pages per Domain SnowflakeWindows Functions 47 Math Expressions SnowflakeRegular Expressions 48 CSV and Partitions SparkDataFrame 49 Repartition SparkDataFrame 50 Broadcast Join SparkDataFrame 51 Correcting Social Media Posts SparkDataFrame 52 Daily Category Sales Aggregation SparkAggregate Functions 53 Cache and Performance SparkDataFrame 54 Filter Popular Videos SparkDataFrame 55 Anonymize User PII SparkDataFrame 56 Call Center Daily Stats SparkDataFrame 57 Venture Capital Sector Analysis SparkJoins 58 Window Functions without Partitions SparkWindows Functions 59 Calculating PE Portfolio Values SparkAggregate Functions 60 Mountain Climber Logs SparkWindow Functions 61 Global & Domain SEO Leaders SparkAggregation 62 Tracking Customer Purchase History SparkWindow Functions 63 Merge Customer Records from Two Sources SnowflakeCombining Data 64 Filter Funded Startups SnowflakeAggregation 65 Assign Row Numbers to Authors per Paper SnowflakeWindows Functions 66 Amusement Park Rating Anomalies SnowflakeAggregate Functions 67 Usage and Accuracy per Model Type SnowflakeAggregation 68 Find the Last Climber per Mountain SnowflakeWindows Functions 69 Track Product Purchases SnowflakeWindows Functions 70 Most Common Order Status SparkDataFrame 71 Calculating Overtime Pay SparkJoins 72 Top Products by Revenue SparkDataFrame 73 Product Summary SparkNull Handling 74 Parsing Comma-Separated Values SparkString Manipulation 75 CSV Row Filter and Count ProgrammingPython 76 Analyze Sales Dataset Dimensions and Calculate Total Revenue ProgrammingPython 77 Sort Avro Employee Records by Salary ProgrammingPython 78 Count User Events from JSON Activity Logs ProgrammingPython 79 Split Delimited Column into Separate Columns with Pandas ProgrammingPython 80 Compare SQLite Database and CSV File Records ProgrammingPython 81 Analyze DataFrame Memory Usage ProgrammingPython 82 Time-Series Rolling Window Analysis for Multi-Stock Price Data ProgrammingPython 83 Flatten Nested JSON to CSV with Dot-Notation Columns ProgrammingPython 84 Calculate Descriptive Statistics for Numeric Columns in Pandas ProgrammingPython 85 Decompose Time-Series Data into Trend, Seasonal, and Residual Components ProgrammingPython 86 Extract Schema Information from Parquet File Using PyArrow ProgrammingPython 87 Select Specific Columns from Parquet File ProgrammingPython 88 Flatten Nested Struct Columns in Parquet and Export to CSV ProgrammingPython 89 Merge Customer and Purchase Data Using Pandas ProgrammingPython 90 SQL JOIN with Pandas Data Processing and CSV Export ProgrammingPython 91 Insert New Records into SQLite Database from CSV ProgrammingPython 92 Aggregate SQL Query Results with Pandas and Export to Excel ProgrammingPython 93 Aggregate Time-Series Data into Fixed Time Windows ProgrammingPython 94 Interpolate Missing Values in Irregular Time-Series Sensor Data ProgrammingPython 95 Remove Seasonal Effects from Time-Series Sales Data ProgrammingPython 96 Convert Excel Files with Multiple Sheets to Individual CSV Files ProgrammingPython