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Category: Data Lake

Understanding the Data Lakehouse

Tom Jordan explains what data lakehouses are:

When we are thinking about data platforms, there are many different services and architectures that can be used – sometimes this can be a bit overwhelming! Data warehouses, data models, data lakes and reports are all typical components of an enterprise data platform, which have different uses and skills required. However, in the past few years a new architecture has been rising; the data lakehouse. This is an architecture that borrows ideas and concepts from several different areas, which we will be exploring in greater detail in this blog.

Click through to learn more about the origin of this term and how it draws + differs from both a data lake and a data warehouse.

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Delta Lake Operability in Azure Synapse Analytics

James Serra lets us know when and where we can use Delta Lake within Azure Synapse Analytics:

Many companies are seeing the value in collecting data to help them make better business decisions. When building a solution in Azure to collect the data, nearly everyone is using a data lake. A majority of those are also using delta lake, which is basically a software layer over a data lake that gives additional features. I have yet to see anyone using competing technologies to delta lake in Azure, such as Apache Hudi or Apache Iceberg (see A Thorough Comparison of Delta Lake, Iceberg and Hudi and Open Source Data Lake Table Formats: Evaluating Current Interest and Rate of Adoption).

Read on for more information.

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Organizing Synapse Workspaces and Lakehouses

Jovan Popovic confirms that Microsoft is using the term “Lakehouse” like Databricks does:

The lakehouse pattern enables you to keep a large amount of your data in Data Lake and to get the analytic capabilities without a need to move your data to some data warehouse to start an analysis. A lakehouse represents a good trade-off between query performance and the ability to access the latest version of data without the need to wait for data to be reloaded.

Azure Synapse Analytics workspace enables you to implement the Lakehouse pattern on top of Azure Data Lake storage.

When you think about your lakehouse solution, be aware that there are two options for creating databases over the lake:

Lake databases that are created using Spark or database template

SQL databases that are created using serverless SQL pools on top of data lake.

Although you might use different tools and languages to create these types of databases, the principles described in this article apply to both types. I will use the term “lakehouse” whenever i reference Spak Lake database or SQL database created using the serverless SQL pools.

Click through for Jovan’s guidance.

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Querying Delta Lake via Azure Synapse Analytics Serverless SQL Pool

Tony Truong uses T -SQL to query Delta Lake files:

How to query Delta Lake with SQL on Azure Synapse  

As mentioned earlier, Azure Synapse has several compute pools for the evolving analytical workload. There is the Apache Spark pool for data engineers and serverless SQL pool for analysts. Let us break down how the two personas work together to query a shared Delta Lake.  

Read on for the setup and the payoff.

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Automatic Backups on a Data Lake or Lakehouse

Dave Ruijter backs that thing up:

Out of the box, Azure Data Lake Storage Gen2 provides redundant storage. Therefore, the data in your Data Lake(house) is resilient to transient hardware failures within a datacenter through automated replicas. This ensures durability and high availability. In this blog post, I provide a backup strategy on how to further protect your data from accidental deletions, data corruption, or any other data failures. This strategy works for Data Lake as well as Data Lakehouse implementations. It uses native Azure services, no additional tools, software, or licenses are required.

Read on for a detailed strategy.

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Creating Delta Lake Tables in Azure Databricks

Gauri Mahajan takes us through creating new tables in a Delta Lake using Azure Databricks:

Delta lake is an open-source data format that provides ACID transactions, data reliability, query performance, data caching and indexing, and many other benefits. Delta lake can be thought of as an extension of existing data lakes and can be configured per the data requirements. Azure Databricks has a delta engine as one of the core components that facilitates delta lake format for data engineering and performance. Delta lake format is used to create modern data lake or lakehouse architectures. It is also used to build a combined streaming and batch architecture popularly known as lambda architecture.

Click through for the process.

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SCD Type 2 with Delta Lake

Chris Williams continues a series on slowly changing dimensions in Delta Lake:

Type 2 SCD is probably one of the most common examples to easily preserve history in a dimension table and is commonly used throughout any Data Warehousing/Modelling architecture. Active rows can be indicated with a boolean flag or a start and end date. In this example from the table above, all active rows can be displayed simply by returning a query where the end date is null.

Read on to see how you can implement this pattern using Delta Lake’s capabilities.

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Change Data Capture in Delta Lake

Surya Sai Turaga and John O’Dwyer take us through change data capture in Delta Lake:

Change data capture (CDC) is a use case that we see many customers implement in Databricks – you can check out our previous deep dive on the topic here. Typically we see CDC used in an ingestion to analytics architecture called the medallion architecture. The medallion architecture that takes raw data landed from source systems and refines the data through bronze, silver and gold tables. CDC and the medallion architecture provide multiple benefits to users since only changed or added data needs to be processed. In addition, the different tables in the architecture allow different personas, such as Data Scientists and BI Analysts, to use the correct up-to-date data for their needs. We are happy to announce the exciting new Change Data Feed (CDF) feature in Delta Lake that makes this architecture simpler to implement and the MERGE operation and log versioning of Delta Lake possible!

Read on to gain an understanding of how it works.

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Data Hubs, Warehouses, and Lakes

Trevor Legg compares and contrasts data hubs, data warehouses, and data lakes:

Data hubs, data warehouses, and data lakes are significant investment areas for data and analytics leaders and are vital to support increasingly complex, distributed, and varied data workloads.

Gartner finds that 57% of data and analytics leaders are investing in data warehouses, 46% are using data hubs, and 39% are using data lakes. However, they also found that these same data and analytics leaders don’t necessarily understand the difference between the three…

To best support specific business requirements, it’s vital to understand the difference and purpose of each type of structure, and the role it can play in modern data management infrastructure.

Click through for the definitions and comparisons.

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