Modern Unified Data Architecture

Today, most business value is derived from the analysis of data and products powered by data, rather than the software itself. The data generated by several application silos are combined and greatly enhanced to provide a better customer experience. Deriving value from the data includes building a unified data architecture and a collaborative effort of data engineering and data science teams. Data engineering involves building and maintaining the data infrastructure and data pipelines, and Data science involves transforming crude data into something useful and deriving insights through analytical and ML workloads.

#big-data #machine-learning #data-architecture #data-science #data-engineering

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Modern Unified Data Architecture
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

Sid  Schuppe

Sid Schuppe

1618404240

Benefits of Hybrid Cloud for Data Warehouse

In today’s market reliable data is worth its weight in gold, and having a single source of truth for business-related queries is a must-have for organizations of all sizes. For decades companies have turned to data warehouses to consolidate operational and transactional information, but many existing data warehouses are no longer able to keep up with the data demands of the current business climate. They are hard to scale, inflexible, and simply incapable of handling the large volumes of data and increasingly complex queries.

These days organizations need a faster, more efficient, and modern data warehouse that is robust enough to handle large amounts of data and multiple users while simultaneously delivering real-time query results. And that is where hybrid cloud comes in. As increasing volumes of data are being generated and stored in the cloud, enterprises are rethinking their strategies for data warehousing and analytics. Hybrid cloud data warehouses allow you to utilize existing resources and architectures while streamlining your data and cloud goals.

#cloud #data analytics #business intelligence #hybrid cloud #data warehouse #data storage #data management solutions #master data management #data warehouse architecture #data warehouses

Modern Unified Data Architecture

Today, most business value is derived from the analysis of data and products powered by data, rather than the software itself. The data generated by several application silos are combined and greatly enhanced to provide a better customer experience. Deriving value from the data includes building a unified data architecture and a collaborative effort of data engineering and data science teams. Data engineering involves building and maintaining the data infrastructure and data pipelines, and Data science involves transforming crude data into something useful and deriving insights through analytical and ML workloads.

#big-data #machine-learning #data-architecture #data-science #data-engineering

Data Lakes Are Not Just For Big Data - DZone Big Data

We recently wrote an article debunking common myths about data lake architectures, data lake definitions, and data lake analytics. It is called "What is a Data Lake_? Get A Leg Up Avoiding The Biggest Myths." _In that article, we framed the current conversation about data lakes and how they fit within enterprise data strategies. This topic has historically been confusing and opaque for those wanting to get value from a data lake due to conflicting advice from consultants and vendors.

One area that can be particularly confusing is the perception that lakes are only for “big data.” If you spend any time reading materials on lakes, you would think there is only one type and it would look like the Capsian Sea (it’s a lake despite “sea” in the name). People describe data lakes as massive, all-encompassing entities, designed to hold all knowledge. The good news is that lakes are not just for “big data” and you have more opportunities than ever to have them be part of your data stack.

Yes, There Are Different Types of Data Lakes

Just as they do in nature, lakes come in all different shapes and sizes. Each has a natural state, often reflecting ecosystems of data, just like those in nature reflect ecosystems of fish, birds, or other organisms.

Unfortunately, the “big data” angle gives the impression that lakes are only for “Caspian” scale data endeavors. This certainly makes the use of data lakes intimidating. As a result, describing things in such massive terms makes the concept of a lake inaccessible to those who can benefit from them on a smaller scale. Here are a few data lake examples;

  • **The Great “Caspian”: ** Just like the Caspian is a large body of water, this type of lake is a large, broad repository-diverse set of data. This broad collection of diverse data reflects information from across the enterprise. This is how most data lake efforts are framed.
  • **Temporary “Ephemeral”: **Just like deserts can have small, temporary lakes, an Ephemeral exists for a short period of time. They may be used for a project, pilot, PoC or a point solution and they are turned off as quickly as they were turned on.
  • **Domain “Project”: **These lakes, like Ephemeral data lakes, are often focused on specific knowledge domains. However, unlike the Ephemeral lake, this lake will persist over time. These may also be “shallow,” meaning they may be focused on a narrow domain of data such as media, social, web analytics, email, or similar data sources.

We recently worked with a customer to create a “Domain” type lake. This lake would hold Adobe event data to an AWS to support an enterprise Oracle Cloud environment. Why AWS to Oracle? It was an efficient and cost-effective data consumption pattern for the customer Oracle BI environment, especially considering the agility and economics of using an AWS lake and Athena as the on-demand query service for lake content.

By design, all types of lakes should embrace an abstraction that minimizes risk and affords you greater flexibility. Also, they should be structured for easy consumption independent of their size. This ensures a lake used by a data scientist or business user or analyst all have an environment structured for easy data consumption.

Getting Started With Data Lakes

Being a successful early adopter means taking a business value approach rather than a technology one. Here are a few tips as you think about how to get started:

  • Focus: Seek opportunities where you can deploy an “Ephemeral” or “Project” solution. This will ensure you reduce risk and overcome technical and organizational challenges so your team can build confidence with lakes.
  • Passion: Make sure you have an “evangelist” or “advocate” internally, someone who is passionate about the solution and adoption within the company.
  • Simple: Embrace simplicity and agility, put people, processes, and technology choices through this lens. The lack of complexity should not be seen as a deficiency but a byproduct of thoughtful design.
  • Narrow: Keep the scope narrow and well defined by limiting your lake to understand data, say exports from ERP, CRM, Point-of-Sales, Marketing, or Advertising data. Data literacy at this stage will help you understand workflow around data structure, ingest, governance, quality, and testing.
  • Experiment: Pair your lake with a modern BI and analytics tools like Tableau, Power BI, Amazon Quicksight, or Looker. This will allow non-technical users an opportunity to experiment and explore data access via a lake. This allows you to engage a different user base that can assess performance bottlenecks, discover opportunities for improvements, possible linkages to any existing EDW systems (or other data systems), and additional candidate data sources.

#big data #data lake #data lakes #data lake architecture #data lake solutions #data analysis