Aisu  Joesph

Aisu Joesph

1622558820

How to use iterations and conditions activities in Azure Data Factory

In the previous articles, we discussed how to create an Azure Data Factory pipeline to copy data between different data stores that are located in on-premises servers or in the cloud, how to transform data using Azure Data factory Mapping Dataflow activity and how to run an SSIS package using Azure Data Factory.

In this article, we will show how to use the Iterations and Conditions activities in the Azure Data Factory.

Demo Overview

In the demo that we will discuss in this article, we will create an Azure Data Factory pipeline, that will read data stored in CSV files located in an Azure Blob Storage container, making sure that the file extension is CSV and the size of the file larger than or equal to 1KB, and write the data to an Azure SQL Database table.

Prerequisites

In order to create that pipeline, make sure that you have an Azure Data Factory, an Azure Storage account where the CSV files are stored, as shown below:

And an Azure SQL Database where the data will be written, where we need to add the current client IP address to the Azure SQL Database firewall settings, in order to be able to connect using SSMS from my machine, and enable Allow Azure Services and Resources to access this Server firewall setting to allow the Azure Data Factory to access it, as shown below:

Getting started

Linked Services and DataSets

The first step in creating the Azure Data Factory pipeline is creating the source and sink linked services and datasets. To achieve that, open the Azure Data Factory, click on Author & Monitor to launch the Data Factory UI.

From the opened Azure Data Factory UI in the Azure Portal, click on the Manage button to create the Linked Services, as shown below:

Now, we need to create the linked service that points to the Azure Blob Storage container where the CSV files are stored. To create the linked service, click on the Linked Services option under the Connections list, then click on the New button, as below:

From the New Linked Service window, choose the Azure Blob Storage data store, then click Continue to proceed:

In the new Azure Blob Storage Linked service window, provide a meaningful name for the linked service, the Integration Runtime that will be used to connect to the Azure Blob Storage, which is Azure IR in our case, the authentication method that will be used to connect to that storage account, the Azure Subscription where this storage account is created and the name of that storage account.

#azure #azure data factory

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How to use iterations and conditions activities in Azure Data Factory
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

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

Aisu  Joesph

Aisu Joesph

1622558820

How to use iterations and conditions activities in Azure Data Factory

In the previous articles, we discussed how to create an Azure Data Factory pipeline to copy data between different data stores that are located in on-premises servers or in the cloud, how to transform data using Azure Data factory Mapping Dataflow activity and how to run an SSIS package using Azure Data Factory.

In this article, we will show how to use the Iterations and Conditions activities in the Azure Data Factory.

Demo Overview

In the demo that we will discuss in this article, we will create an Azure Data Factory pipeline, that will read data stored in CSV files located in an Azure Blob Storage container, making sure that the file extension is CSV and the size of the file larger than or equal to 1KB, and write the data to an Azure SQL Database table.

Prerequisites

In order to create that pipeline, make sure that you have an Azure Data Factory, an Azure Storage account where the CSV files are stored, as shown below:

And an Azure SQL Database where the data will be written, where we need to add the current client IP address to the Azure SQL Database firewall settings, in order to be able to connect using SSMS from my machine, and enable Allow Azure Services and Resources to access this Server firewall setting to allow the Azure Data Factory to access it, as shown below:

Getting started

Linked Services and DataSets

The first step in creating the Azure Data Factory pipeline is creating the source and sink linked services and datasets. To achieve that, open the Azure Data Factory, click on Author & Monitor to launch the Data Factory UI.

From the opened Azure Data Factory UI in the Azure Portal, click on the Manage button to create the Linked Services, as shown below:

Now, we need to create the linked service that points to the Azure Blob Storage container where the CSV files are stored. To create the linked service, click on the Linked Services option under the Connections list, then click on the New button, as below:

From the New Linked Service window, choose the Azure Blob Storage data store, then click Continue to proceed:

In the new Azure Blob Storage Linked service window, provide a meaningful name for the linked service, the Integration Runtime that will be used to connect to the Azure Blob Storage, which is Azure IR in our case, the authentication method that will be used to connect to that storage account, the Azure Subscription where this storage account is created and the name of that storage account.

#azure #azure data factory

Eric  Bukenya

Eric Bukenya

1624735500

Deploying Azure Data Factory using Bicep

So after a long break (caused by a combination of laziness and actually being busy), I’m attempting to get back into creating more video content.

In this video, I’m using Bicep to deploy Azure Data Factory! Bicep is Domain Specific Language for deploying resources to Azure and is a HUGE improvement over ARM templates.

I’ve been trying to learn Bicep for my new job. Usually when it comes to Infrastructure code, I’d opt for Terraform since that’s the first IaC tool I learnt. In my new role, I’m using ARM a lot more.

To be honest, I never really liked ARM. Messing about with JSON files wasn’t my idea of fun, so when I first saw Bicep I was keen to give it a go.

#cloud-computing #data #azure #bicep #azure data factory #deploying

Data Lake and Data Mesh Use Cases

As data mesh advocates come to suggest that the data mesh should replace the monolithic, centralized data lake, I wanted to check in with Dipti Borkar, co-founder and Chief Product Officer at Ahana. Dipti has been a tremendous resource for me over the years as she has held leadership positions at Couchbase, Kinetica, and Alluxio.

Definitions

  • A data lake is a concept consisting of a collection of storage instances of various data assets. These assets are stored in a near-exact, or even exact, copy of the resource format and in addition to the originating data stores.
  • A data mesh is a type of data platform architecture that embraces the ubiquity of data in the enterprise by leveraging a domain-oriented, self-serve design. Mesh is an abstraction layer that sits atop data sources and provides access.

According to Dipti, while data lakes and data mesh both have use cases they work well for, data mesh can’t replace the data lake unless all data sources are created equal — and for many, that’s not the case.

Data Sources

All data sources are not equal. There are different dimensions of data:

  • Amount of data being stored
  • Importance of the data
  • Type of data
  • Type of analysis to be supported
  • Longevity of the data being stored
  • Cost of managing and processing the data

Each data source has its purpose. Some are built for fast access for small amounts of data, some are meant for real transactions, some are meant for data that applications need, and some are meant for getting insights on large amounts of data.

AWS S3

Things changed when AWS commoditized the storage layer with the AWS S3 object-store 15 years ago. Given the ubiquity and affordability of S3 and other cloud storage, companies are moving most of this data to cloud object stores and building data lakes, where it can be analyzed in many different ways.

Because of the low cost, enterprises can store all of their data — enterprise, third-party, IoT, and streaming — into an S3 data lake. However, the data cannot be processed there. You need engines on top like Hive, Presto, and Spark to process it. Hadoop tried to do this with limited success. Presto and Spark have solved the SQL in S3 query problem.

#big data #big data analytics #data lake #data lake and data mesh #data lake #data mesh