Since last week Snowflake went public. Snowflake now is publicly available for trade on the New York Stock Exchange. What this Snowflake IPO means is explained on the Snowflake blog by Frank Slootman, Snowflake’s CEO.

“Frank Slootman completes his trilogy”

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With the the Snowflake IPO, CEO Frank Slootman completes his trilogy. Snowflake’s CEO was already enjoying his long earned retirement. In his career he already brought two different IT companies to the public stock exchange. Both Data Domain (2102) as well as ServiceNow (2017) went public under his watch. …


Snowflake GROUP BY data Herous
Snowflake GROUP BY data Herous

Today, Wednesday 15 July, I had the privilege to present at the first online Snowflake ❄️ GROUP BY Data Heroes online event. For me personally a great opportunity to get more involved into the Snowflake Community. A fantastic chance to help others and myself to get better.

Special thanks to Angelica Coleman, Elsa Mayer an Kent Graziano for the organisation and the support.

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“GROUP BY is an opportunity for data practitioners around the world to come together virtually and learn how some of the most engaged users are leveraging Snowflake. …


DaAnalytics taking Snowflake SnowPro Core exams in the Pong office wit polar bear support
DaAnalytics taking Snowflake SnowPro Core exams in the Pong office wit polar bear support

Since early 2018 I have developed an interest in Snowflake, the Data Warehouse built for the Cloud. Although I have been working with Oracle for almost the whole of my professional live, it made perfect sense to me to also have a look at Snowflake. Not in the last place because some of the founders came from Oracle. When I started looking at Snowflake, I knew I wanted to get certified too. Last year at the first Snowflake Summit in San Francisco I received the first one; Hands-On Snowflake — Web UI. Now it was time to go for the second one. Last week passed my exams. …


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Last week I wrote about the newly released Matillion Data Loader (MDL). Matillion Data Loader is a Code-free, Wizard-based Pipeline builder. My first MDL Data Pipeline was loading an Excel sheet into Snowflake. Creating Data Pipelines in Matillion Data Loader is relatively easy and straightforward. Time to try something else. This time I will be loading BigQuery (GBQ) into Snowflake. Again I will show how to go from Source to Cloud Data Warehouse in a few clicks.

Add Pipeline

First add the Google Big Query Pipeline to select data from GBQ.

Add Google Big Query Data Pipeline
Add Google Big Query Data Pipeline

Destination

Add Snowflake Cloud DWH destinat
Add Snowflake Cloud DWH destinat

To be able to load data into Snowflake, you need to make sure that MDL can connect to Snowflake with the correct credentials and with enough privileges. Therefor you can re-use a Snowflake Destination Configuration if you created one earlier. I re-use the one I created for the previous blogpost. …


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Introducing Matillion Data Loader to load data from GCP into SnowflakeAs you might know I am managing the Data & Analytics unit at Pong. One of my responsibilities is setting the direction of this unit. One of our focus points is Modern Cloud Analytics. Therefor we have setup a few partnerships focussed on the Snowflake Eco-system. You can find out more about these partnerships; here. Unfortunately only in Dutch, but Google Translate might help a bit. Matillion is one of those partners from the Snowflake Eco-system.

A few weeks ago I found out that Matillion is launching Matillion Data Loader (MDL); a free Software-as-a-Service (SaaS) Data Integration solution. …


Find scripts and snippets in the DaAnalytics-github, to setup and get started with Snowflake. This peace is inspired by the following blogpost by Venkatesh Sekar from Hashmapinc.

In this folder the following scripts:

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Next, follow Venkatesh Sekar’s tips and try the following:

Good luck setting up your own Snowflake Sandbox. If you run into any issues, please let me know. I will do my upmost to help you out.

Thanks for reading.

Cheers

Originally published at http://www.daanalytics.blog.


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In de wereld van Data & Analytics gaat het vaak over oplossingen in de Cloud. Wat is dat dan, de Cloud? Heel simpel gezegd; De Cloud is de computer van iemand anders. De waarheid ligt iets gecompliceerder. Men spreekt bij de Cloud over; ‘as-a-Service’. Een Cloud provider (Amazon, Microsoft, Google, Oracle, etc.) levert een service, die je af kunt nemen en waar je (naar gelang gebruik) voor betaalt. Geen investering vooraf.

As-a-Service

Grofweg verdelen we de Cloud in 3 smaken:
1. Software-as-a-Service (SaaS) — Eindgebruikers gebruiken software zoals Office 365
2. Platform-as-a-Service (PaaS) — Ontwikkelaars gebruiken een BI Platform zoals Looker en Oracle Analytics
3. …


Gisterenavond was de kick-off van ‘ Cloud Analytics Now’. Dit is een initiatief van Johan van der Kooij en Rogier Werschkull.

Cloud Analytics Now — De Kick-off
Cloud Analytics Now — De Kick-off
Cloud Analytics Now — De Kick-off

Johan van der Kooij (de founding father van BI Dutch) gaf een korte introductie van het hoe en het waarom van ‘Cloud Analytics Now’. In goed Nederlands: “With the rise of fully managed, as-a-service cloud solutions for your data pipeline, data orchestration, analytics and data science, we believe the time to start your data / analytics migration to the cloud is now!”.

Rogier nam daarna het stokje over. Hij gaf, uit eigen ervaringen, een vergelijk tussen een aantal verschillende Cloud Analytical Databases; Amazon Redshift, Azure SQL DWH, Snowflake en Google BigQuery. Al voor dat de sessie begonnen was, leidde dit al tot een discussie. Waarom géén Azure Synapse Analytics? Daar is al eens eerder een en ander over geschreven. Volg die discussie hier. Waarom géén Teradata Vantage? Rogiers antwoord was even simpel als eerlijk. Hij heeft de tools gekozen op basis van zijn ervaring. De reden om interesse te hebben in een aantal producten had ook te maken met hun plaatsen in verschillende online rapporten van onder andere; G2 en Forrester. Zijn onderzoek is niet compleet (in de zin dat er nog veel meer leveranciers zijn) en ook zeker niet representatief. Daar deed hij ook niet ingewikkeld over. Geïnteresseerd in andere (onafhankelijke) onderzoeken? …


As promised in a previous blog, I will have a look at the SqlDBM partnership with Snowflake. According to SqlDBM it “offers you an easy, convenient way to design your database absolutely anywhere on any browser, working away without need for any extra database engine or database modelling tools or apps”. With SqlDBM you can both Forward Engineer (generate SQL) as well as Reverse Engineer (import an existing Database Schema). Check out more details about the the SqlDBM partnership with Snowflake here.

Using SqlDBM is as easy as navigating to; https://sqldbm.com/Home/ and sign-in (or sign-up first).

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SqlDBM — SQL Database Modeler

Forward Engineer

With Forward Engineering, SqlDBM makes it possible to generate DDL statements. These DDL statements can be executed in Snowflake to create the physical data model (tables, columns, relationships, schema properties, file format options, column constraints, etc.). …


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AWS S3 to Snowflake

In the serie; “Third-Partying with Snowflake” we will go and load some data into Snowflake. There are various ways to do that. This blog will cover the selecting data in a AWS S3 bucket and copy these into Snowflake. Check out the Snowflake docs on how to to this. I extended that doc with some my own experiences (and struggles). This will be a step-by-step procedure. You might follow along.

Loading data into AWS S3 Bucket

First we have to load some data into AWS S3. Find some sample data here on Github. …

About

Daan Bakboord

Data & Analytics 📊 Consultant @daanalytics_nl & Partner @pongprof & Board Member @nl_OUG | Modern ☁️ Analytics #Oracle #Snowflake #Looker #Matillion #Fivetran

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