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Category: Warehousing

Azure Synapse Analytics Sample Datasets and Scripts

James Serra shows us where to find samples for Azure Synapse Analytics:

Datasets: A bunch of datasets that when added will show up under Data -> Linked -> Azure Blob Storage.  You can then choose an action (via “…” next to any of the containers in the dataset) and choose New SQL script -> Select TOP 100 rows to examine the data as well as choose “New notebook” to load the data into a Spark dataframe.  Any dataset you add is a linked service to files in a blob storage container using SAS authentication.  You can also create an external table in a SQL on-demand pool or SQL provisioned pool to each dataset via an action (via “…” next to “External tables” under the database, then New SQL script -> New external table) and then query it or insert the data into a SQL provisioned database

Click through to learn more, as well as a few other things you can do with Synapse Analytics.

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An Introduction to Data Vault

Tino Zishiri walks us through the basics of the Data Vault modeling technique:

The Data Vault methodology also addresses a common limitation that relates to the dimensional model approach. There are many good things to say about dimensional modelling, it’s a perfect fit for doing analytics, it’s easy for business analysts to understand, it’s performant over large sets of data, the list goes on.

That said, the data vault methodology addresses the limitations of having a “fixed” model. Dimensional modelling’s resilience to change or “graceful extensibility”, as some would say, is well documented. It’s capable of handling changing data relationships which can be implemented without affecting existing BI apps or query results. For example, facts consistent with the grain of an existing fact table can be added by creating new columns. Moreover, dimensions can be added to an existing fact table by creating new foreign key columns, presuming they don’t alter the fact table’s grain.

The most interesting thing to me about Data Vault is that it’s very popular in Europe and almost unheard-of in North America. That’s the impression I get, at least.

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Data Lineage and SSIS

Aveek Das has a two-parter. First up is a discussion of data lineage:

In this article, I am going to explain what Data Lineage in ETL is and how to implement the same. In this modern world, where companies are dealing with a humongous amount of data every day, there also lies a challenge to efficiently manage and monitor this data. There are systems that generate data every second and are being processed to a final reporting or monitoring tool for analysis. In order to process this data, we use a variety of ETL tools, which in turn makes the data transformation possible in a managed way.

While transforming the data in the ETL pipeline, it has to go through multiple steps of transformations in order to achieve the final result. For example, when the ETL receives the raw data from the source, there may be operations applied to it like filtering, sorting, merging, or splitting two columns, etc. There can also be aggregations or other calculations made on this raw data before finally moving into a data warehouse or preparing it for reporting. In order to be able to detect what the source of a particular record is, we need to implement something known as Data Lineage. It is a piece of simple metadata information that helps us detect gaps in the data processing pipeline and enables us to fix issues later.

Part two covers data lineage with SQL Server Integration Services:

In this article, I am going to discuss SSIS data lineage concepts, which are often used while designing ETL workloads on a data warehouse. Although this article is focused on implementing data lineage using SSIS, it does not only confine to SSIS but to any ETL tools in the market using which data is moved from one source to a destination. In my previous article, Understanding Data Lineage in ETL, I have already discussed the generic importance of data lineage concepts for any ETL tool. I would definitely suggest you have a look at it if you want to understand in general how data lineage helps to track the source of a single record in the warehouse.

If you’re fairly new to this world, it’s a good introduction to an important topic.

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A Review of Azure Synapse Analytics

Teo Lachev looks at Azure Synapse Analytics:

There is plenty to like in Azure Synapse which is the evaluation of Azure SQL DW. If you’re tasked to implement a cloud-based data warehouse, you have a choice among three Azure SQL Server-based PaaS offerings, including Azure SQL Database, Azure SQL Managed Instance, and Azure Synapse. In a nutshell, Azure SQL Database and Azure SQL MI are optimized for OLTP workloads. For example, they have full logging enabled and replicate each transaction across replicas. Full logging is usually a no-no for decent size DW workloads because of the massive ETL changes involved.

In addition, to achieve good performance, you’ll find yourself moving up the performance tiers and toward the price point of the lower Azure Synapse SKUs. Not to mention that unlike Azure SQL Database, Azure Synapse can be paused, such as when reports hit a semantic layer instead of DW, and this may offer additional cost cutting options.

Teo focuses primarily on SQL Pools and the more SQL-friendly side of things (ELT and Power BI) rather than Spark pools.

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SQL Serverless in Azure Synapse Analytics

James Serra talks to us about SQL serverless (presently known as SQL on-demand but I’m getting ahead of the marketing curve this time):

Querying data in ADLS Gen2 storage using T-SQL is made easy because of the OPENROWSET function with additional capabilities (check out the T-SQL that is supported). The currently supported file types in ADLS Gen2 that SQL-on-demand can use are Parquet, CSV, and JSON. ParquetDirect and CSV 2.0 add performance improvements (see Benchmarking Azure Synapse Analytics – SQL Serverless, using .NET Interactive). You can also query folders and multiple files and use file metadata in queries.

Read on to learn a lot more about its use cases.

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Historical Dimensions in a Kimball-Style Model

Vince Iacoboni takes a stab at improving the Kimball model:

We owe a lot to Ralph Kimball and friends. His practical warehouse design and conformed-dimension bus architecture are the industry standard. Business users can understand and query these warehouses directly and gain valuable insights into the business. Kimball’s practical approach focuses squarely on clarity and ease of use for the business users of the warehouse. Kudos to you and yours, Mr. Kimball.

That said, can the mainstay Type 2 slowly changing dimension be improved? I here present the concept of historical dimensions as a way to solve some issues with the basic Type 2 slowly changing dimension promoted by Kimball. As we will see, clearly distinguishing between current and past dimension values pays off in clarity of design, flexibility of presentation, and ease of ETL maintenance.

As I was reading this, I was thinking “This sounds like a type 4 SCD” and Vince walks us through the differences between the two ideas. I’m not absolutely sold on the idea, but it is certainly interesting.

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Managing Lakehouse Data

Harsha Gummadavelli gives us an introduction to the Data Lakehouse concept:

“Data Lakehouse” is a new architecture paradigm in the data management space that combines the best characteristics of Data Warehouse and Data Lakes. Once you load the data into a data lake, there is no need to load the data into a warehouse for additional analysis or business intelligence. You can directly query the data residing in cheaper but highly reliable storage, often termed as “Object Stores”, thus reducing the operational overhead on data pipelines.

I will say that I’m not particularly sold on the data lakehouse concept at this point. It’s interesting, in that it reduces the number of systems to maintain by one, but I do wonder about performance issues when trying to replace an existing warehouse. The post turns into a marketing pitch for Informatica, but the first half does give a fair introduction to the concept.

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Trust and Warehouse Data

Rob Farley explains one way that people might lose trust in your warehouse data:

The scenario is that there’s a source system, and there’s a table in a warehouse that is being used to report on it. Maybe it’s being populated by Integration Services or Data Factory. Maybe it’s being populated by T-SQL. I don’t really care. What I care about is whether the data in the warehouse is a true representation of what’s in the source system.

If it’s not a true representation, then we have all kinds of problems.

Mostly, that our warehouse is rubbish.

Read on for an example of how this might occur and what you can do to prevent it.

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A Brief Overview of Azure Synapse Analytics

Ginger Grant gives us the nickel tour of why Azure Synapse Analytics is interesting:

In the past few months, I have been examining Azure Synapse and what it can do.  When it was first released in November of 2019, the first functionality that was rolled out was an update of Azure SQL DW.  For this reason, many people think that Synapse is just an improved version of a cloud data warehouse.  Microsoft did improve SQL DW when it moved it to Synapse.  The biggest architectural design change is the separation of the code from the compute, a theme with many web projects, which allows the compute power to be increased when need dictates and scaled down when computing needs change.  Within Synapse, resources are allocated as Pools and you can define a sql pools to run data warehouse and later change the compute to a different resource.  You will still need to partition your DW as large datasets require partitioning to perform well.  Subsequently Microsoft Released the Azure Synapse Studio to be a container for a larger environment of tools and notebooks to interact with them.

But it’s more than that. Read on to see what else is available.

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Query Scheduling with Apache Hive

Zoltan Haindrich and Jesus Camacho Rodriguez walk us through scheduled queries in Apache Hive:

To fulfill that purpose, recently Apache Hive introduced a new feature called scheduled queries. Using SQL statements, users can schedule Hive queries to run on a recurring basis, monitor their progress, and optionally disable a query schedule.

In a nutshell, every scheduled query in Hive consists of (i) a unique name to identify the schedule, (ii)  the actual SQL statement to be executed, and (iii) the schedule at which the query should be executed defined by a Quartz cron expression. In addition, a scheduled query belongs to a namespace, i.e., a collection of HiveServer2 instances that are responsible to execute the query.

Read on for examples of how you might create, use, and learn about scheduled queries running on a system.

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