1603184400
Introduction: Data-driven workflows in Microsoft Azure Data Fatory.
Azure Data Factory
The need to trigger the batch movement of data and prepare a regular schedule is a requirement for most analytics solutions, this can be achieved by using a cloud-based data integration service, that orchestrates the movement and transformation of data.
Azure Data Factory (ADF) is one of the cloud-based ETL and data integration service that allows you to create data-driven workflows for orchestrating data movement and transforming data at scale. Using Azure Data Factory, you can create and schedule data-driven workflows (called pipelines) that can ingest data from disparate data stores. You can build complex ETL processes that transform data visually with data flows or by using compute services that exists in Azure.
Img. 1 — Data factory cloud compute and storage integrations.
What is meant by orchestration?
To use an analogy, think about a symphony orchestra. The central member of the orchestra is the conductor. The conductor does not play the instruments, they simply lead the symphony members through the entire piece of music that they perform. The musicians use their own skills to produce particular sounds at various stages of the symphony, so they may only learn certain parts of the music. The conductor orchestrates the entire piece of music, and therefore is aware of the entire score that is being performed. They will also use specific arm movements that provide instructions to the musicians how a piece of music should be played.
ADF can use a similar approach, whilst it has native functionality to ingest and transform data, sometimes it will instruct another service to perform the actual work required on its behalf, such as a Databricks to execute a transformation query. So, in this case, it would be Databricks that performs the work, not ADF. ADF merely orchestrates the execution of the query, and then provides the pipelines to move the data onto the next step or destination.
#etl #azure #analytics #azure-data-factory #data-engineering #data-science
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1608022715
Top Microsoft big data solutions Companies | Best Microsoft big data Developers
An extensively researched list of top Microsoft big data analytics and solution with ratings & reviews to help find the best Microsoft big data solutions development companies around the world.
An exclusive list of Microsoft Big Data consulting and solution providers, after examining various factors of expert big data analytics firms and found the equivalent matches that boast the ace qualities with proven fineness in data analytics. For business growth and enterprise acceleration getting inputs from the whole data of the organization have become necessary, thus we bring to you the most trustworthy Microsoft Big Data consultants and solutions providers for your assistance.
Let’s take a look at the List of Best Microsoft big data solutions Companies.
#microsoft big data solutions development companies #microsoft big data analytics and solution #microsoft big data consultants #microsoft big data developers #microsoft big data #microsoft big data solution providers
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
1603184400
Introduction: Data-driven workflows in Microsoft Azure Data Fatory.
Azure Data Factory
The need to trigger the batch movement of data and prepare a regular schedule is a requirement for most analytics solutions, this can be achieved by using a cloud-based data integration service, that orchestrates the movement and transformation of data.
Azure Data Factory (ADF) is one of the cloud-based ETL and data integration service that allows you to create data-driven workflows for orchestrating data movement and transforming data at scale. Using Azure Data Factory, you can create and schedule data-driven workflows (called pipelines) that can ingest data from disparate data stores. You can build complex ETL processes that transform data visually with data flows or by using compute services that exists in Azure.
Img. 1 — Data factory cloud compute and storage integrations.
What is meant by orchestration?
To use an analogy, think about a symphony orchestra. The central member of the orchestra is the conductor. The conductor does not play the instruments, they simply lead the symphony members through the entire piece of music that they perform. The musicians use their own skills to produce particular sounds at various stages of the symphony, so they may only learn certain parts of the music. The conductor orchestrates the entire piece of music, and therefore is aware of the entire score that is being performed. They will also use specific arm movements that provide instructions to the musicians how a piece of music should be played.
ADF can use a similar approach, whilst it has native functionality to ingest and transform data, sometimes it will instruct another service to perform the actual work required on its behalf, such as a Databricks to execute a transformation query. So, in this case, it would be Databricks that performs the work, not ADF. ADF merely orchestrates the execution of the query, and then provides the pipelines to move the data onto the next step or destination.
#etl #azure #analytics #azure-data-factory #data-engineering #data-science
1618521240
Only Data-Minded Marketers and Market-Minded Developers Can Achieve Data Driven Marketing
Using data as a part of your marketing plan can have a tremendous impact on your overall results, which is why data-driven marketing has become the standard for many agencies.
However, data-driven marketing may require many businesses to rethink the way they work, especially when it comes to cooperation between their various teams.
You may have heard about the concept of collaboration and automating processes before - something referred to as webops. Now an increasing number of companies are throwing marketing into the mix.
Among the most important factors is a close working relationship between marketing and web development teams if a business wants to make the most of data-driven marketing.
#data-driven #data-driven-marketing #web-development #marketing-data-science #teamwork #data-driven-development #data-driven-decision-making #webops
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