Dejah  Reinger

Dejah Reinger

1603198800

Transforming schema drifted CSV files into relational data in Azure Databricks

Data is the blood of a business. Data comes in varying shapes and sizes, making it a constant challenging task to find the means of processing and consumption, without which it holds no value whatsoever. This article looks at how to leverage Apache Spark’s parallel analytics capabilities to iteratively cleanse and transform schema drifted CSV files into queryable relational data to store in a data warehouse. We will work in a Spark environment and write code in PySpark to achieve our transformation goal.

Disclaimer and Terms of use

Please read our terms of use before proceeding with this article.

Caution

Microsoft Azure is a paid service, and following this article can cause financial liability to you or your organization.

Prerequisites

1. An active Microsoft Azure subscription

2. Azure Data Lake Storage Gen2 storage with CSV files

3. Azure Databricks Workspace (Premium Pricing Tier)

4. Azure Synapse Analytics data warehouse

If you don’t have prerequisites set up yet, refer to our previous articles to get started.

Sign in to the Azure Portal, locate and open your Azure Databricks instance and click on ‘Launch Workspace.’ Our Databricks instance will open up in a new browser tab; wait for Azure AD SSO to sign you in automatically.

#azure-databricks #data-cleansing #pyspark #schema-drift #data-science

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Transforming schema drifted CSV files into relational data in Azure Databricks
Dejah  Reinger

Dejah Reinger

1603198800

Transforming schema drifted CSV files into relational data in Azure Databricks

Data is the blood of a business. Data comes in varying shapes and sizes, making it a constant challenging task to find the means of processing and consumption, without which it holds no value whatsoever. This article looks at how to leverage Apache Spark’s parallel analytics capabilities to iteratively cleanse and transform schema drifted CSV files into queryable relational data to store in a data warehouse. We will work in a Spark environment and write code in PySpark to achieve our transformation goal.

Disclaimer and Terms of use

Please read our terms of use before proceeding with this article.

Caution

Microsoft Azure is a paid service, and following this article can cause financial liability to you or your organization.

Prerequisites

1. An active Microsoft Azure subscription

2. Azure Data Lake Storage Gen2 storage with CSV files

3. Azure Databricks Workspace (Premium Pricing Tier)

4. Azure Synapse Analytics data warehouse

If you don’t have prerequisites set up yet, refer to our previous articles to get started.

Sign in to the Azure Portal, locate and open your Azure Databricks instance and click on ‘Launch Workspace.’ Our Databricks instance will open up in a new browser tab; wait for Azure AD SSO to sign you in automatically.

#azure-databricks #data-cleansing #pyspark #schema-drift #data-science

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

Data Storage in 2021: Choosing the Right Tools for the Job

Reading about the death of the relational database seems like a regular occurrence. However, here we are in 2021, and the relational data store is going strong. If we look at the DB-Engines Ranking website, six of the top 10, including the top four spots, are all relational data stores. Evidently, structured, or relational, data storage is here to stay. Yet four of the top spots are held by non-relational engines. Could that mean that relational data storage is really dying?

The core of this question is not really which kind of data store is better between a relational, normalized structure or a non-relational, denormalized storage mechanism. No, the real core question is: What kind of data store should your organization be using in 2021?

The shortest possible way I can answer this question is as follows:

All of them.

The fact is, you’re much better off not trying to answer your data needs with one, single methodology. Let’s discuss why.

#nosql #sql #data storage #relational database #data consistency #relational data #structured data #non-relational database #data stores #database choice

Trevor  Russel

Trevor Russel

1618472340

Microsoft Azure Data Lake

2020 is different in every way, but one thing is constant for the past many years i.e. data and its role in molding our current technology. Recently, I was part of the team to create a central controlled data repository containing clear, consistent, and clean data. While exploring the technologies we landed on MS Azure echo system.

MS Azure echo system for developing data lakes/data warehouse is becoming mature and providing good support when it comes to the enterprise-level solutions. Starting from Azure Data Factory, it gave a good ELT/ETL processing with code-free services. This is very helpful to create pipelines for data ingestion, control flow, and moving data from source to destination. These pipelines have the capability to run 24/7 and ingest petabytes of data. Without the support of a data factory data movement between different enterprise systems requires a lot of effort and at times will be very expensive to develop and maintain. Additionally, there are more than 90 built-in connectors in Azure Data Factory which will help to connect with most of the sources like S3, Redshift, BigQuery, HDFS, Salesforce, and enterprise data warehouse to name a few.

#big data #data + integration #data streaming #big data adoption #data transformation #microsft azure