Press "Enter" to skip to content

Category: Architecture

Comparing Oracle RAC to SQL Server Availability Groups

Published 2019-03-29 by Kevin Feasel

Kellyn Pot’vin-Gorman explains the difference between Oracle RAC and SQL Server Availability Groups:

There is a constant rumble among Oracle DBAs- either all-in for Oracle Real Application Cluster, (RAC) or a desire to use it for the tool it was technically intended for. Oracle RAC can be very enticing- complex and feature rich, its the standard for engineered systems, such as Oracle Exadata and even the Oracle Data Appliance, (ODA). Newer implementation features, such as Oracle RAC One-Node offered even greater flexibility in the design of Oracle environments, but we need to also discuss what it isn’t- Oracle RAC is not a Disaster Recovery solution.

Click through for a good high-level contrast, as these are quite different products.

Comments closed

Contrasting Oracle’s Architecture with SQL Server’s

Published 2019-03-22 by Kevin Feasel

Kellyn Pot’vin-Gorman helps us understand where Oracle’s architecture differs from SQL Server’s:

The first thing you’ll notice is what Oracle refers to as an INSTANCE is different to what SQL Server calls one.

Oracle’s instance is most closely related to what SQL Server calls their database, (although it includes the files that Oracle includes in their description, too) and the Oracle home is *relatively* SQL Server’s version of an instance.

Read on for a deeper comparison from someone who has spent quite a bit of time working with both platforms.

Comments closed

Database-First or Kafka-First for Event Streaming

Published 2019-03-19 by Kevin Feasel

Gwen Shapiro takes us through a scenario where database-first writes for event streaming makes the most sense:

Note that the DB does quite a lot for you: it enforces serializability, locks, your logical constraints, etc. If the DB is distributed (Vitesse, Cockroach, Spanner, Yugabyte), it does even more.

If you were to go Kafka-first… well, it isn’t impossible. But all those responsibilities now belong to you as a developer. And if you are thinking there may be multiple webservers handling user requests and passing them to Kafka, you have to solve fairly challenging problems.

Read the whole thing.

Comments closed

Flattening Dimensional Models

Published 2019-03-18 by Kevin Feasel

Reza Rad explains why it makes sense to build flat dimensional models, particularly for Power BI:

The article that I wrote earlier this week about the shared dimension had a lot of interest, and I’m glad it helped many of you. So I thought better to write about the basics of modeling even more. In this article, I will be focusing on a scenario that you have all faced, however, took different approaches. Is it good to have too many dimension tables? can you combine some of those tables together to build one flatten dimension table? how much should you flatten it? should you end up with one huge table including everything? In this article, I’m answering all of these questions and explaining the scenarios of combining dimensions, as usual, I explain the model in Power BI. However, the concepts are applicable to any other tools. If you like to learn more about Power BI; read Power BI book from Rookie to Rock Star.

Given how closely the ideal Power BI data model matches the Kimball model, Reza’s advice makes perfect sense.

Comments closed

Securely Accessing External Resources From Databricks AWS

Published 2019-03-13 by Kevin Feasel

Itai Weiss shows how you can securely hit external data sources when using Databricks for AWS:

For security purposes, Databricks Apache Spark clusters are deployed in an isolated VPC dedicated to Databricks within the customer’s account. In order to run their data workloads, there is a need to have secure connectivity between the Databricks Spark Clusters and the above data sources.

It is straightforward for Databricks clusters located within the Databricks VPC to access data from AWS S3 which is not a VPC specific service. However, we need a different solution to access data from sources deployed in other VPCs such as AWS Redshift, RDS databases, streaming data from Kinesis or Kafka. This blog will walk you through some of the options you have available to access data from these sources securely and their cost considerations for deployments on AWS. In order to establish a secure connection to these data sources, we will have to configure the Databricks VPC with either one of the following two available options :

Read on for those two options.

Comments closed

Monitoring Kafka Streams with JMX Metrics

Published 2019-03-07 by Kevin Feasel

Rishi Khandelwal provides a reference architecture for monitoring a Kafka Streams application using JMX Metrics and pushing the results into Graphite:

Service (application) exposes the JMX metrics at some port which will be captured by Jolokia java agent. Then Jolokia exposes those metrics at some port which is easily accessible through a rest endpoint (we call it Jolokia URL). Then we have JMX2Graphte which polls the metrics from Jolokia URL and push it to Graphite. Then Grafana reads the Graphite metrics and creates a beautiful dashboard for us along with the alerts.

So this is the working of the proposed monitoring solution. Now let’s discuss the components of the monitoring solution.

There’s a bit of code/configuration in here as well, so check it out.

Comments closed

Microservice Communication Patterns

Published 2019-02-26 by Kevin Feasel

John Hammink shares a few ways that you can have microservices communicate with one another and argues that Kafka is a great platform for microservice communication:

Simply put, microservices are a software development method where applications are structured as loosely coupled services. The services themselves are minimal atomic units which together, comprise the entire functionality of the entire app. Whereas in an SOA, a single component service may combine one or several functions, a microservice within an MSA does one thing — only one thing — and does it well.

Microservices can be thought of as minimal units of functionality, can be deployed independently, are reusable, and communicate with each other via various network protocols like HTTP (More on that in a moment).

Read the whole thing. I have a love-hate relationship with these but it’s a pattern worth understanding.

Comments closed

Power BI Architecture Diagram V4

Published 2019-02-18 by Kevin Feasel

Dustin Ryan has a new version of the Power BI Architecture Diagram:

First and most importantly, I updated the Power BI logo in the diagram to the latest version of the logo!

Secondly, I included Power BI Dataflows in the diagram tagged #6. Power BI Dataflows are used to ingest, transform, integrate, and enrich big data by defining data source connections, ETL logic, refresh schedules, and more. Data is stored as entities in the Common Data Model in Azure Data Lake Storage Gen2. Dataflow entities can be consumed as a data source in Power BI and by using Power BI Desktop. Read more about Dataflows here.

Click through for a full changelog and a link to download the architecture diagram and legend.

Comments closed

Kafka And The Differing Aims Of Data Professionals

Published 2019-02-15 by Kevin Feasel

Kai Waehner argues that there is an impedence mismatch between data engineers, data scientists, and ML production engineers:

Data scientists love Python, period. Therefore, the majority of machine learning/deep learning frameworks focus on Python APIs. Both the stablest and most cutting edge APIs, as well as the majority of examples and tutorials use Python APIs. In addition to Python support, there is typically support for other programming languages, including JavaScript for web integration and Java for platform integration—though oftentimes with fewer features and less maturity. No matter what other platforms are supported, chances are very high that your data scientists will build and train their analytic models with Python.

There is an impedance mismatch between model development using Python, its tool stack and a scalable, reliable data platform with low latency, high throughput, zero data loss and 24/7 availability requirements needed for data ingestion, preprocessing, model deployment and monitoring at scale. Python in practice is not the most well-known technology for these requirements. However, it is a great client for a data platform like Apache Kafka.

Click through for the full argument as well as where Kafka can help mitigate some of the issues.

Comments closed

Reporting Services Scale-Out With Docker

Published 2019-01-30 by Kevin Feasel

Paul Stanton architects out a scenario using Windocks to create cloned Reporting Services containers in order to scale out Reporting Services:

Database cloning is a key aspect of the SSRS scale out architecture, with database clones providing each container a complete set of databases.  Two or more VMs operated behind a load balancer delivers a highly available and scalable reporting service.  This article focuses on Windows SQL Server containers and Windows Virtual Hard Drive (VHD) based cloning, but the same architecture can support SQL Server Linux containers or conventional instances (Windows or Linux).   Redgate SQL Clone, for example would support SQL Server instances.   Other options include the use of storage arrays instead of Windows VHD based clones.   The trade-offs between SQL containers and instances, and between VHDs and storage arrays are covered in separate sections below. 

The combination of SSRS containers with database cloning is appealing for simplicity and operational savings.  SSRS containers are also drawing interest as part of public cloud strategies, as SSRS containers can be integrated with AWS RDS or SQL Azure databases to provide a horizontally scalable reporting solution.

This is a bit more complex than Reporting Services scale-out with Enterprise Edition, but if you’re on Standard Edition and can’t use scale-out, it’s an interesting alternative.

Comments closed