DP-203 Data Engineering on Microsoft Azure Questions and Answers

The Delta provides the ability to infer the schema for data input which further reduces the effort required in managing the schema changes. The Slowly Changing Data(SCD) Type 2 records all the changes made to each key in the dimensional table. These operations require updating the existing rows to mark the previous values of the keys as old and then inserting new rows as the latest values. Also, Given a source table with the updates and the target table with dimensional data, SCD Type 2 can be expressed with the merge.

Example:

// Implementing SCD Type 2 operation using merge function

customersTable

as("customers")

merge(

stagedUpdates.as("staged_updates"),

"customers.customerId = mergeKey")

whenMatched("customers.current = true AND customers.address <> staged_updates.address")

updateExpr(Map(

"current" -> "false",

"endDate" -> "staged_updates.effectiveDate"))

whenNotMatched()

insertExpr(Map(

"customerid" -> "staged_updates.customerId",

"address" -> "staged_updates.address",

"current" -> "true",

"effectiveDate" -> "staged_updates.effectiveDate",

"endDate" -> "null"))

execute()

}

Box 1: LAG

The LAG analytic operator allows one to look up a “previous” event in an event stream, within certain constraints. It is very useful for computing the rate of growth of a variable, detecting when a variable crosses a threshold, or when a condition starts or stops being true.

Box 2: LIMIT DURATION

Example: Compute the rate of growth, per sensor:

SELECT sensorId,

growth = reading -

LAG(reading) OVER (PARTITION BY sensorId LIMIT DURATION(hour, 1))

FROM input

Box 1: Move to cool storage

Box 2: Move to archive storage

Archive - Optimized for storing data that is rarely accessed and stored for at least 180 days with flexible latency requirements, on the order of hours.

The following table shows a comparison of premium performance block blob storage, and the hot, cool, and archive access tiers.

Clustered columnstore indexes are one of the most efficient ways you can store your data in dedicated SQL pool.

Columnstore tables won't benefit a query unless the table has more than 60 million rows.

Box 1: succeed

Box 2: failed

Example:

Now let’s say we have a pipeline with 3 activities, where Activity1 has a success path to Activity2 and a failure path to Activity3. If Activity1 fails and Activity3 succeeds, the pipeline will fail. The presence of the success path alongside the failure path changes the outcome reported by the pipeline, even though the activity executions from the pipeline are the same as the previous scenario.

Activity1 fails, Activity2 is skipped, and Activity3 succeeds. The pipeline reports failure.

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Box 1: Parquet

For Parquet datasets, the type property of the copy activity source must be set to ParquetSource.

Box 2: PreserveHierarchy

PreserveHierarchy (default): Preserves the file hierarchy in the target folder. The relative path of the source file to the source folder is identical to the relative path of the target file to the target folder.

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Answer is below

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-wareh ouse/sql-data-warehouse-tables-partition

Need a minimum 1 million rows per distribution. Each table is 60 distributions. 30 millions rows is added each month. Need 2 months to get a minimum of 1 million rows per distribution in a new partition.

Note: When creating partitions on clustered columnstore tables, it is important to consider how many rows belong to each partition. For optimal compression and performance of clustered columnstore tables, a minimum of 1 million rows per distribution and partition is needed. Before partitions are created, dedicated SQL pool already divides each table into 60 distributions.

Any partitioning added to a table is in addition to the distributions created behind the scenes. Using this example, if the sales fact table contained 36 monthly partitions, and given that a dedicated SQL pool has 60 distributions, then the sales fact table should contain 60 million rows per month, or 2.1 billion rows when all months are populated. If a table contains fewer than the recommended minimum number of rows per partition, consider using fewer partitions in order to increase the number of rows per partition.

Avro supports batch and is very relevant for streaming.

Note: Avro is framework developed within Apache’s Hadoop project. It is a row-based storage format which is widely used as a serialization process. AVRO stores its schema in JSON format making it easy to read and interpret by any program. The data itself is stored in binary format by doing it compact and efficient.

CI/CD lifecycle

• A development data factory is created and configured with Azure Repos Git. All developers should have permission to author Data Factory resources like pipelines and datasets.
• A developer creates a feature branch to make a change. They debug their pipeline runs with their most recent changes
• After a developer is satisfied with their changes, they create a pull request from their feature branch to the main or collaboration branch to get their changes reviewed by peers.
• After a pull request is approved and changes are merged in the main branch, the changes get published to the development factory.

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Box 1: Round-robin

Round-robin tables are useful for improving loading speed.

Scenario: Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month.

Box 2: Hash

Hash-distributed tables improve query performance on large fact tables.

Scenario: Implement a surrogate key to account for changes to the retail store addresses.

A surrogate key on a table is a column with a unique identifier for each row. The key is not generated from the table data. Data modelers like to create surrogate keys on their tables when they design data warehouse models. You can use the IDENTITY property to achieve this goal simply and effectively without affecting load performance.

Scenario: Allow Contoso users to use PolyBase in an Azure Synapse Analytics dedicated SQL pool to query the content of the data records that host the Twitter feeds. Data must be protected by using row-level security (RLS). The users must be authenticated by using their own Azure AD credentials.

Box 1: CREATE EXTERNAL DATA SOURCE

External data sources are used to connect to storage accounts.

Box 2: CREATE EXTERNAL FILE FORMAT

CREATE EXTERNAL FILE FORMAT creates an external file format object that defines external data stored in Azure Blob Storage or Azure Data Lake Storage. Creating an external file format is a prerequisite for creating an external table.

Box 3: CREATE EXTERNAL TABLE AS SELECT

When used in conjunction with the CREATE TABLE AS SELECT statement, selecting from an external table imports data into a table within the SQL pool. In addition to the COPY statement, external tables are useful for loading data.

Scenario: Purge Twitter feed data records that are older than two years.

Data sets have unique lifecycles. Early in the lifecycle, people access some data often. But the need for access often drops drastically as the data ages. Some data remains idle in the cloud and is rarely accessed once stored. Some data sets expire days or months after creation, while other data sets are actively read and modified throughout their lifetimes. Azure Storage lifecycle management offers a rule-based policy that you can use to transition blob data to the appropriate access tiers or to expire data at the end of the data lifecycle.

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Box 1: Configure Evegent Hubs partitions

Scenario: Maximize the throughput of ingesting Twitter feeds from Event Hubs to Azure Storage without purchasing additional throughput or capacity units.

Event Hubs is designed to help with processing of large volumes of events. Event Hubs throughput is scaled by using partitions and throughput-unit allocations.

Event Hubs traffic is controlled by TUs (standard tier). Auto-inflate enables you to start small with the minimum required TUs you choose. The feature then scales automatically to the maximum limit of TUs you need, depending on the increase in your traffic.

Box 2: An Azure Data Lake Storage Gen2 account

Scenario: Ensure that the data store supports Azure AD-based access control down to the object level.

Azure Data Lake Storage Gen2 implements an access control model that supports both Azure role-based access control (Azure RBAC) and POSIX-like access control lists (ACLs).

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Box 1: Create table

Scenario: Load the sales transaction dataset to Azure Synapse Analytics

Box 2: RANGE RIGHT FOR VALUES

Scenario: Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong to the partition on the right.

RANGE RIGHT: Specifies the boundary value belongs to the partition on the right (higher values).

FOR VALUES ( boundary_value [,...n] ): Specifies the boundary values for the partition.

Scenario: Load the sales transaction dataset to Azure Synapse Analytics.

Contoso identifies the following requirements for the sales transaction dataset:

• Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong to the partition on the right.
• Ensure that queries joining and filtering sales transaction records based on product ID complete as quickly as possible.
• Implement a surrogate key to account for changes to the retail store addresses.
• Ensure that data storage costs and performance are predictable.
• Minimize how long it takes to remove old records.

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Box 1: Hash

Scenario:

Ensure that queries joining and filtering sales transaction records based on product ID complete as quickly as possible.

A hash distributed table can deliver the highest query performance for joins and aggregations on large tables.

Box 2: Set the distribution column to the sales date.

Scenario: Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong to the partition on the right.

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Scenario: Identify a process to ensure that changes to the ingestion and transformation activities can be version-controlled and developed independently by multiple data engineers.

Step 1: Create a repository and a main branch

You need a Git repository in Azure Pipelines, TFS, or GitHub with your app.

Step 2: Create a feature branch

Step 3: Create a pull request

Step 4: Merge changes

Merge feature branches into the main branch using pull requests.

Step 5: Publish changes

Box 1: Sales date

Scenario: Contoso requirements for data integration include:

• Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong to the partition on the right.

Box 2: An Azure Synapse Analytics Dedicated SQL pool

Scenario: Contoso requirements for data integration include:

• Ensure that data storage costs and performance are predictable.

The size of a dedicated SQL pool (formerly SQL DW) is determined by Data Warehousing Units (DWU).

Dedicated SQL pool (formerly SQL DW) stores data in relational tables with columnar storage. This format significantly reduces the data storage costs, and improves query performance.

Synapse analytics dedicated sql pool

Data Factory stores pipeline-run data for only 45 days. Use Azure Monitor if you want to keep that data for a longer time.

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Box 1: MAX

The first step on the query finds the maximum time stamp in 10-minute windows, that is the time stamp of the last event for that window. The second step joins the results of the first query with the original stream to find the event that match the last time stamps in each window.

Query:

WITH LastInWindow AS

(

SELECT

MAX(Time) AS LastEventTime

FROM

Input TIMESTAMP BY Time

GROUP BY

TumblingWindow(minute, 10)

)

SELECT

Input.License_plate,

Input.Make,

Input.Time

FROM

Input TIMESTAMP BY Time

INNER JOIN LastInWindow

ON DATEDIFF(minute, Input, LastInWindow) BETWEEN 0 AND 10

AND Input.Time = LastInWindow.LastEventTime

Box 2: TumblingWindow

Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals.

Box 3: DATEDIFF

DATEDIFF is a date-specific function that compares and returns the time difference between two DateTime fields, for more information, refer to date functions.

https://docs.azuredat abricks.net/spark/latest/data-sources/azure/adls-passthrough.html

Box 1: Hash-distributed

Box 2: ProductKey

ProductKey is used extensively in joins.

Hash-distributed tables improve query performance on large fact tables.

Box 3: Round-robin

Box 4: RegionKey

Round-robin tables are useful for improving loading speed.

Consider using the round-robin distribution for your table in the following scenarios:

• When getting started as a simple starting point since it is the default
• If there is no obvious joining key
• If there is not good candidate column for hash distributing the table
• If the table does not share a common join key with other tables
• If the join is less significant than other joins in the query
• When the table is a temporary staging table

Note: A distributed table appears as a single table, but the rows are actually stored across 60 distributions. The rows are distributed with a hash or round-robin algorithm.

Scenario: All cloud data must be encrypted at rest and in transit.

Always Encrypted is a feature designed to protect sensitive data stored in specific database columns from access (for example, credit card numbers, national identification numbers, or data on a need to know basis). This includes database administrators or other privileged users who are authorized to access the database to perform management tasks, but have no business need to access the particular data in the encrypted columns. The data is always encrypted, which means the encrypted data is decrypted only for processing by client applications with access to the encryption key.

References:

https://docs.microsoft. com/en-us/azure/sql-database/sql-database-security-overview

Virtual network rules are one firewall security feature that controls whether the database server for your single databases and elastic pool in Azure SQL Database or for your databases in SQL Data Warehouse accepts communications that are sent from particular subnets in virtual networks.

Server-level, not database-level: Each virtual network rule applies to your whole Azure SQL Database server, not just to one particular database on the server. In other words, virtual network rule applies at the serverlevel, not at the database-level.

References:

https://docs.microsoft.com/en-us/azure/sql-database/sql-database-vnet-service-endpoint-rule-overvi ew

Guarantee Stream Analytics job reliability during service updates

Part of being a fully managed service is the capability to introduce new service functionality and improvements at a rapid pace. As a result, Stream Analytics can have a service update deploy on a weekly (or more frequent) basis. No matter how much testing is done there is still a risk that an existing, running job may break due to the introduction of a bug. If you are running mission critical jobs, these risks need to be avoided. You can reduce this risk by following Azure’s paired region model.

Scenario: The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer loyalty number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure

Box 1: Self-hosted integration runtime

A self-hosted IR is capable of running copy activity between a cloud data stores and a data store in private network.

Box 2: Schedule trigger

Schedule every 8 hours

Box 3: Copy activity

Scenario:

• Customer data, including name, contact information, and loyalty number, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table.
• Product data, including product ID, name, and category, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table.