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

Deploying Azure Data Factory using Bicep
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

Deploying Azure Data Factory using Bicep
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

Your Data Architecture: Simple Best Practices for Your Data Strategy
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

Getting Started With Data Lakes

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

Data Lake and Data Mesh Use Cases
Aisu  Joesph

Aisu Joesph

1626490533

Azure Series #2: Single Server Deployment (Output)

No organization that is on the growth path or intending to have a more customer base and new entry into the market will restrict its infrastructure and design for one Database option. There are two levels of Database selection

  • a.  **The needs assessment **
  • **b. Selecting the kind of database **
  • c. Selection of Queues for communication
  • d. Selecting the technology player

Options to choose from:

  1. Transactional Databases:
    • Azure selection — Data Factory, Redis, CosmosDB, Azure SQL, Postgres SQL, MySQL, MariaDB, SQL Database, Maria DB, Managed Server
  2. Data warehousing:
    • Azure selection — CosmosDB
    • Delta Lake — Data Brick’s Lakehouse Architecture.
  3. Non-Relational Database:
  4. _- _Azure selection — CosmosDB
  5. Data Lake:
    • Azure Data Lake
    • Delta Lake — Data Bricks.
  6. Big Data and Analytics:
    • Data Bricks
    • Azure — HDInsights, Azure Synapse Analytics, Event Hubs, Data Lake Storage gen1, Azure Data Explorer Clusters, Data Factories, Azure Data Bricks, Analytics Services, Stream Analytics, Website UI, Cognitive Search, PowerBI, Queries, Reports.
  7. Machine Learning:
    • Azure — Azure Synapse Analytics, Machine Learning, Genomics accounts, Bot Services, Machine Learning Studio, Cognitive Services, Bonsai.

Key Data platform services would like to highlight

  • 1. Azure Data Factory (ADF)
  • 2. Azure Synapse Analytics
  • 3. Azure Stream Analytics
  • 4. Azure Databricks
  • 5. Azure Cognitive Services
  • 6. Azure Data Lake Storage
  • 7. Azure HDInsight
  • 8. Azure CosmosDB
  • 9. Azure SQL Database

#azure-databricks #azure #microsoft-azure-analytics #azure-data-factory #azure series

Azure Series #2: Single Server Deployment (Output)