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Category: Warehousing

Reverse ETL in a Modern Data Warehouse

James Serra reverses the polarity:

“Reverse ETL” is the process of moving data from a modern data warehouse into third party systems to make the data operational. Traditionally data stored in a data warehouse is used for analytical workloads and business intelligence (i.e. identify long-term trends and influencing long-term strategy), but some companies are now recognizing that this data can be further utilized for operational analytics. Operational analytics helps with day-to-day decisions with the goal of improving the efficiency and effectiveness of an organization’s operations. In simpler terms, it’s putting a company’s data to work so everyone can make better and smarter decisions about the business. As examples, if your MDW ingested customer data which was then cleaned and mastered, that customer data can then by copied into multiple SaaS systems such as Salesforce to make sure there is a consistent view of the customer across all systems. Customer info can also be copied to a customer support system to provide better support to that customer by having more info about that person, or copied to a sales system to give the customer a better sales experience. As a last example, you can identify at-risk customers by surfacing customer usage data in a CRM.

Click through for more details, including information on a few startups working on reverse ETL projects.

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EDW: Maintenance is Costlier than Development

Andy Leonard argues that with an Enterprise Data Warehouse, development is the less expensive side of the coin:

Crossing the threshold between “bad data” and “data that is too bad” is somewhat dependent on how the data is being used by the enterprise.

Don’t let that simplistic-sounding response trip you up. Please recognize two truths about the sentence above:

1. The sentence above has nothing to do with math.
2. The sentence above has everything to do with enterprise culture; specifically, enterprise data culture.

Read the whole thing.

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Durable Keys in Type 2 Dimensions

Martin Schoombee takes us through the idea of durable keys:

Also called an immutable or persisted key (I like durable better), a durable key is nothing more than a surrogate key (i.e. integer value or nonsensical number) used to identify a dimension member (company, employee, etc.) uniquely in a type-2 dimension. Confusing enough? It’s easier to explain with an example…

When I read Martin’s post, I kind of got it but said to myself, “How would I run this type of query more efficiently?” The thing that wasn’t clicking came from another article on the topic: you add the durable key to the fact as well as the current key. That way, you can join back to the Company dimension on CompanyKey if you want to get the company data as of the fact date, or you can join on DurableCompanyKey (and CurrentRecord = 1) to get the latest company data regardless of the fact date. Now that this is clear, I like the strategy a lot.

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Real-Time Data Warehousing in Cloudera

Justin Hayes gives us an overview of using Cloudera Data Platform for real-time data warehousing:

The simplest way to describe a RTDW is that it looks and feels like a normal data warehouse, but everything is faster even while massive scale is maintained. It is a type of data warehouse modernization that lets you have “small data” semantics and performance at “big data” scale.

– the data arrives into the warehouse faster – think streams of many millions of events per second constantly arriving

– the time it takes for the data to be optimally queryable is faster – query immediately upon arrival with no need for processing or aggregation or compaction

– the speed at which queries run is faster – small, selective queries are measured in 10s or 100s of milliseconds; large, scan- or compute-heavy queries are processed at very high bandwidth

– mutations of the data, when needed, are fast – if data needs to be corrected or updated for whatever reason, this can be done in place without large rewrites

Read on for more.

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Tips for Optimizing Dedicated SQL Pools in Synpase Analytics

Tsuyoshi Matsuzaki shares some tips for improving query performance when using Dedicated SQL Pools in Azure Synapse Analytics:

By above BROADCAST_MOVE operation, the rows in dimension_City table are all copied in a temporary table (called TEMP_ID_3) on all distributed database. (See below.)
Since the size of dimension_City is small, then all rows in this table is duplicated in all database before joining. This time, we join only 2 tables, however, if a lot of tables are needed to join, this data movement will become large overhead for query execution.

The short version is, replicate smaller dimensions and align distribution keys for large tables which get joined together. Both of these minimize the changes of the engine needing to shuffle data between nodes. These sorts of things can make a huge difference when working with Dedicated SQL Pools, cutting query time down by an order of magnitude in some extreme cases.

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Integrating Power BI into Azure Synapse Analytics

Ginger Grant walks us through two methods of integrating Power BI and Azure Synapse Analytics:

From within Synapse you have the ability to access a Power BI workspace so that you can use Power BI from within Synapse.  Your Power BI tenant can be in a different data center than the Azure Synapse Workspace, but they both must be in the same Power BI Tenant.  You can use Power BI to look at any data you wish, as the data you use can be from any location. When this blog was written, it was only possible to connect to one Power BI workspace from within Azure Synapse. In order to run Power BI as shown here, first I needed to create a Linked Service from within Synapse.

Read on for more.

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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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