Getting Started With Azure Data Explorer Using the Go SDK

With the help of an example, this blog post will walk you through how to use the Azure Data explorer Go SDK to ingest data from an Azure Blob storage container and query it programmatically using the SDK. After a quick overview of how to setup Azure Data Explorer cluster (and a database), we will explore the code to understand what’s going on (and how) and finally test the application using a simple CLI interface

The sample data is a CSV file that can be downloaded from here.

What Is Azure Data Explorer?

Azure Data Explorer (also known as Kusto) is a fast and scalable data exploration service for analyzing large volumes of diverse data from any data source, such as websites, applications, IoT devices, and more. This data can then be used for diagnostics, monitoring, reporting, machine learning, and additional analytics capabilities.

It supports several ingestion methods, including connectors to common services like Event Hub, programmatic ingestion using SDKs, such as .NET and Python, and direct access to the engine for exploration purposes. It also integrates with analytics and modeling services for additional analysis and visualization of data using tools such as Power BI

Go SDK for Azure Data Explorer

The Go client SDK allows you to query, control and ingest into Azure Data Explorer clusters using Go. Please note that this is for interacting with the Azure Data Explorer cluster (and related components such as tables etc.). To create Azure Data Explorer clusters, databases etc. you should the use the admin component (control plane) SDK which is a part of the larger Azure SDK for Go

API docs - https://godoc.org/github.com/Azure/azure-kusto-go

Before getting started, here is what you would need to try out the sample application

#tutorial #big data #azure #analytics #go #azure data #azure data explorer

What is GEEK

Buddha Community

Getting Started With Azure Data Explorer Using the Go SDK

Getting Started With Azure Data Explorer Using the Go SDK

With the help of an example, this blog post will walk you through how to use the Azure Data explorer Go SDK to ingest data from an Azure Blob storage container and query it programmatically using the SDK. After a quick overview of how to setup Azure Data Explorer cluster (and a database), we will explore the code to understand what’s going on (and how) and finally test the application using a simple CLI interface

The sample data is a CSV file that can be downloaded from here.

What Is Azure Data Explorer?

Azure Data Explorer (also known as Kusto) is a fast and scalable data exploration service for analyzing large volumes of diverse data from any data source, such as websites, applications, IoT devices, and more. This data can then be used for diagnostics, monitoring, reporting, machine learning, and additional analytics capabilities.

It supports several ingestion methods, including connectors to common services like Event Hub, programmatic ingestion using SDKs, such as .NET and Python, and direct access to the engine for exploration purposes. It also integrates with analytics and modeling services for additional analysis and visualization of data using tools such as Power BI

Go SDK for Azure Data Explorer

The Go client SDK allows you to query, control and ingest into Azure Data Explorer clusters using Go. Please note that this is for interacting with the Azure Data Explorer cluster (and related components such as tables etc.). To create Azure Data Explorer clusters, databases etc. you should the use the admin component (control plane) SDK which is a part of the larger Azure SDK for Go

API docs - https://godoc.org/github.com/Azure/azure-kusto-go

Before getting started, here is what you would need to try out the sample application

#tutorial #big data #azure #analytics #go #azure data #azure data explorer

Jolie  Reichert

Jolie Reichert

1596948420

How to Use Azure Go SDK to Manage Azure Data Explorer Clusters

Getting started with Azure Data Explorer using the Go SDK covered how to use the Azure Data Explorer Go SDK to ingest and query data from azure data explorer to ingest and query data. In this blog you will the Azure Go SDK to manage Azure Data Explorer clusters and databases.

Azure Data Explorer (also known as Kusto) is a fast and scalable data exploration service for analyzing large volumes of diverse data from any data source, such as websites, applications, IoT devices, and more. This data can then be used for diagnostics, monitoring, reporting, machine learning, and additional analytics capabilities.

In case you’re wondering, we are talking about two different SDKs here. The one covered in this blog is for resource administration (also known as the control plane SDK) and the the one I used in the other post is data plane SDK for interacting with the Azure Data Explorer service itself (ingestion, query etc.)

What’s Covered?

A simple CLI application is used as an example to demonstrate how to use the Go SDK. We’ll try out the application first and go through how to:

  • Create and list Azure Data Explorer clusters
  • Create and list databases in that cluster
  • Delete the database and cluster

Once that’s done, we’ll walk through the sample code to understand what’s going on

The code is available on GitHub https://github.com/abhirockzz/azure-go-sdk-for-dataexplorer

Please note that this CLI based example is just meant to showcase how to use the Azure Go SDK (in the context of Azure Data Explorer) as a part of a larger application. It is not supposed to replace/substitute the Azure CLI which can be used to manage Azure Data Explorer resources

Pre-requisites

Install Go 1.13 or above

You will need a Microsoft Azure account. Go ahead and sign up for a free one!

Install the Azure CLI if you don’t have it already (should be quick!)

#tutorial #big data #azure #analytics #go #golang #azure data explorer clusters #azure go sdk

Nabunya  Jane

Nabunya Jane

1621849440

How to use Azure Go SDK to manage Azure Data Explorer clusters

Getting started with Azure Data Explorer using the Go SDK covered how to use the Azure Data Explorer Go SDK to ingest and query data from azure data explorer to ingest and query data. In this blog you will the Azure Go SDK to manage Azure Data Explorer clusters and databases.

Azure Data Explorer (also known as Kusto) is a fast and scalable data exploration service for analyzing large volumes of diverse data from any data source, such as websites, applications, IoT devices, and more. This data can then be used for diagnostics, monitoring, reporting, machine learning, and additional analytics capabilities.

In case you’re wondering, we are talking about two different SDKs here. The one covered in this blog is for resource administration (also known as the control plane SDK) and the one I used in the other post is data plane SDK for interacting with the Azure Data Explorer service itself (ingestion, query etc.)

What’s covered?

A simple CLI application is used as an example to demonstrate how to use the Go SDK. We’ll try out the application first and go through how to:

  • Create and list Azure Data Explorer clusters
  • Create and list databases in that cluster
  • Delete the database and cluster

Sample CLI app: goadx

Once that’s done, we’ll walk through the sample code to understand what’s going on

_The code is available on GitHub _https://github.com/abhirockzz/azure-go-sdk-for-dataexplorer

Please note that this CLI based example is just meant to showcase how to use the Azure Go SDK (in the context of Azure Data Explorer) as a part of a larger application. It is not supposed to replace/substitute the Azure CLI which can be used to manage Azure Data Explorer resources

#cloud #go #big-data #azure #azure data explorer

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition