A Fine Slice Of SQL Server
The Hadoop in Real World team shows how you can save Spark DataFrames as Hive tables:
It is a very common use case to process the data in Spark and save the processed data or Spark dataframe directly into a Hive table.
There are a couple ways to achieve this.
Click through for those techniques.
Comments closedYou have a data lake that contains employee and social feed data. You have data residing in an employee folder that is used by HR team members and twitter for live social feeds that is usually used by marketing folks. If you use SAS token or RBAC, you cannot control to the folder level.
How do you allow users to perform data exploration using synapse serverless with fine grain control on underlying storage.
Read on for one solution.
Comments closedGauri Mahajan crosses the clouds:
Data exists in various types of formats and hosted in equally varied type repositories depending on the format of data. With the advent of the cloud, generally, every public cloud provider like Amazon AWS, Microsoft Azure, and Google Cloud Platform provides a variety of data hosting avenues. A large-scale enterprise is very likely to have a multi-cloud footprint, where data is hosted on more than one cloud. As the data footprint on the cloud becomes larger, the need for a data catalog becomes increasingly important. Each cloud provider provides its format of the cloud-native data catalog. But even after employing a cloud provider-specific catalog, an enterprise may struggle to get a 360-degree view of data on the cloud. The reason being each cloud provider providers the ability to catalog data hosted on its own cloud only, which compels the end-users to reconcile or reference data from multiple catalogs, as the data catalog on the cloud may not integrate with cross-cloud data hosting services. As the client needs evolve, so do the cloud services. Azure Purview is the brand-new data catalog and governance-related service on the Azure cloud. It has introduced features to support cataloging data hosted on AWS Simple Storage Service (S3), which is typically considered the storage layer of data lake on AWS.
Read on to see how.
Comments closedMax Trinidad shows off a couple of products:
For more information click on Azure Dashboard. You can create up to 100 ‘Private’ dashboards per user.
The next tool has recently got some attention: AzViz PowerShell module. This is module has the ability to generate a visual representation of your Azure Resources.
Read on to see both tools in action.
Comments closedIn reality, you can create a dacpac for a database that’s inside an Azure Synapse Analytics dedicated SQL Pool using a lot of the methods that you use to create them for SQL Server databases.
Azure Data Studio can be an appealing alternative SQL Server Data Tools (SSDT) for tasks like this. Due to various reasons. For instance, it’s a multi-platform solution that is easy to install.
With this in mind, I decided in this post to cover how to create a dacpac for an Azure Synapse Analytics dedicated SQL Pool using Azure Data Studio.
Click through to see how.
Comments closedErik Darling talks about a pet peeve of mine:
I see people writing stored procedures that use local variables either because they read that all functions in a where clause are dangerous, or because they need to perform a calculation on one of the parameters that get passed in.
In nearly all cases, you’re better off… not doing that.
Here are a couple examples of why.
I think a lot of this stems from advice to avoid non-SARGable predicates, but miss the idea that expressions of the sort Erik shows are perfectly cromulent.
Comments closedChris Webb reviews a free tool from Microsoft:
The most impressive thing about it is not what it does but how it does it: a lot of tools claim to make machine learning easy for non-technical users but Lobe really is easy to use. My AI/ML knowledge is very basic but I got up and running with it extremely quickly.
To test it out I downloaded lots of pictures of English churches and trained a model to detect whether the church had a tower or a spire. After I labelled the pictures appropriately:
Click through for Chris’s findings. Looks like the only thing it does today is image classification, but more functionality is forthcoming.
Comments closedLinkedIn runs its big data analytics on Hadoop. During the last five years, the analytics infrastructure has experienced tremendous growth, almost doubling every year in data size, compute workloads, and in all other dimensions. It recently reached two important milestones.
1. LinkedIn now stores 1 exabyte of total data across all Hadoop clusters.
2. Our largest 10,000-node cluster stores 500 PB of data. It maintains 1 billion objects (directories, files, and blocks) on a single NameNode serving RPCs with an average latency under 10 milliseconds, making it one of the largest (if not the largest) Hadoop cluster in the industry.
From the early days of LinkedIn, Apache Hadoop was the basis of our analytics infrastructure. Many teams assisted in this effort to make Hadoop our canonical big data platform.
Read on for different techniques they’ve used, as well as code changes implemented in HDFS to support this data size.
Comments closedIf you missed the first part of the series here is the link here: Query Performance – Part 1 Migrating Azure Analysis Services to Power BI Premium Per User – Reporting/Analytics Made easy with FourMoo and Power BI
In this blog post I am going to investigate how well does PPU scale when comparing it to AAS.
When comparing AAS to PPU, I must find the same size AAS size to what we get with PPU.
Read on for Gibert’s findings.
Comments closedPaul Turley continues a series on doing Power BI the right way:
I was just talking to a consulting client about the best approach to build a data model and he told me something very interesting about the way they were loading data into Power BI. He said “We don’t use facts and dimensions, we load all of our data into one huge table.” He said that their data model performs well and that it meets their reporting needs. It is a difficult point to argue, when something is working at the time although the design might not follow the accepted rules. Life is like that and there are plenty of analogies to make the point that a practice, even a real bad practice, might solve a problem for a period of time and under certain conditions. <analogy>You can drive a car at excessive speed to get to your destination faster. You might not get caught by the police on that day and you might not crash but eventually, if you make it a habit, this practice will catch up to you.</analogy> Data is like that. If you don’t play by the rules, you limit your options. Bending the rules lets you move faster and sometimes with less hassle. But, as the project scope expands – and after adding enough data or other complexities to the solution, it will not endure. The data model won’t perform well, won’t load the correct data or it just won’t be reliable.
This post will explore the realities of best practice design for large data models; some important considerations and trade-off decisions when working with both “big data” and “large data”.
Read on for Paul’s tips.
Comments closed