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Data storage has been evolving, from databases to data warehouses and expansive datalakes, with each architecture responding to different business and data needs. Traditional databases excelled at structured data and transactional workloads but struggled with performance at scale as data volumes grew.
Whether it’s unifying transactional and analytical data with Hybrid Tables, improving governance for an open lakehouse with Snowflake Open Catalog or enhancing threat detection and monitoring with Snowflake Horizon Catalog , Snowflake is reducing the number of moving parts to give customers a fully managed service that just works.
A comparative overview of data warehouses, datalakes, and data marts to help you make informed decisions on data storage solutions for your dataarchitecture.
The goal of this post is to understand how data integrity best practices have been embraced time and time again, no matter the technology underpinning. In the beginning, there was a data warehouse The data warehouse (DW) was an approach to dataarchitecture and structured data management that really hit its stride in the early 1990s.
More than 50% of data leaders recently surveyed by BCG said the complexity of their dataarchitecture is a significant pain point in their enterprise. As a result,” says BCG, “many companies find themselves at a tipping point, at risk of drowning in a deluge of data, overburdened with complexity and costs.”
It’s not enough for businesses to implement and maintain a dataarchitecture. The unpredictability of market shifts and the evolving use of new technologies means businesses need more data they can trust than ever to stay agile and make the right decisions.
Summary The current trend in data management is to centralize the responsibilities of storing and curating the organization’s information to a data engineering team. This organizational pattern is reinforced by the architectural pattern of datalakes as a solution for managing storage and access.
Summary Building and maintaining a datalake is a choose your own adventure of tools, services, and evolving best practices. The flexibility and freedom that datalakes provide allows for generating significant value, but it can also lead to anti-patterns and inconsistent quality in your analytics.
Over the years, the technology landscape for data management has given rise to various architecture patterns, each thoughtfully designed to cater to specific use cases and requirements. Each of these architectures has its own unique strengths and tradeoffs. The schema of semi-structured data tends to evolve over time.
But, even with the backdrop of an AI-dominated future, many organizations still find themselves struggling with everything from managing data volumes and complexity to security concerns to rapidly proliferating data silos and governance challenges.
Summary The Presto project has become the de facto option for building scalable open source analytics in SQL for the datalake. Another area that has been seeing a lot of activity is datalakes and projects to make them more manageable and feature complete (e.g. Hudi, Delta Lake, Iceberg, Nessie, LakeFS, etc.).
Data has continued to grow both in scale and in importance through this period, and today telecommunications companies are increasingly seeing dataarchitecture as an independent organizational challenge, not merely an item on an IT checklist. Previously, there were three types of data structures in telco: .
In this episode he explains how it is designed to allow for querying and combining data where it resides, the use cases that such an architecture unlocks, and the innovative ways that it is being employed at companies across the world.
Modern dataarchitectures. To eliminate or integrate these silos, the public sector needs to adopt robust data management solutions that support modern dataarchitectures (MDAs). Deploying modern dataarchitectures. Lack of sharing hinders the elimination of fraud, waste, and abuse. Forrester ).
In this episode Kevin Liu shares some of the interesting features that they have built by combining those technologies, as well as the challenges that they face in supporting the myriad workloads that are thrown at this layer of their data platform. Can you describe what role Trino and Iceberg play in Stripe's dataarchitecture?
Snowflake is now making it even easier for customers to bring the platform’s usability, performance, governance and many workloads to more data with Iceberg tables (now generally available), unlocking full storage interoperability. Iceberg tables provide compute engine interoperability over a single copy of data.
Note : Cloud Data warehouses like Snowflake and Big Query already have a default time travel feature. However, this feature becomes an absolute must-have if you are operating your analytics on top of your datalake or lakehouse. It can also be integrated into major data platforms like Snowflake.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or datalake. How has the move to the cloud for data warehousing/data platforms influenced the practice of data modeling?
CDC tools fuel analytical apps and mission-critical data feeds in banking and regulated industries, with use cases ranging from data synchronization, managing risk, and preventing fraud to driving personalization. Unlike datalakes, which are predominantly append-only, lakehouses support data mutation natively.
In August, we wrote about how in a future where distributed dataarchitectures are inevitable, unifying and managing operational and business metadata is critical to successfully maximizing the value of data, analytics, and AI.
Gone are the days of just dumping everything into a single database; modern dataarchitectures typically use a combination of datalakes and warehouses. Think of your datalake as a vast reservoir where you store raw data in its original form—great for when you’re not quite sure how you’ll use it yet.
Using the metaphor of a museum curator carefully managing the precious resources on display and in the vaults, he discusses the various layers of an enterprise data strategy. Can you walk through the stages of an ideal lifecycle for data within the context of an organizations uses for it?
Anyways, I wasn’t paying enough attention during university classes, and today I’ll walk you through data layers using — guess what — an example. Business Scenario & DataArchitecture Imagine this: next year, a new team on the grid, Red Thunder Racing, will call us (yes, me and you) to set up their new data infrastructure.
At Precisely’s Trust ’23 conference, Chief Operating Officer Eric Yau hosted an expert panel discussion on modern dataarchitectures. The group kicked off the session by exchanging ideas about what it means to have a modern dataarchitecture.
Today we want to introduce Fivetran’s support for Amazon S3 with Apache Iceberg, investigate some of the implications of this feature, and learn how it fits into the modern dataarchitecture as a whole. Fivetran today announced support for Amazon Simple Storage Service (Amazon S3) with Apache Iceberg datalake format.
Over the past few years, datalakes have emerged as a must-have for the modern data stack. But while the technologies powering our access and analysis of data have matured, the mechanics behind understanding this data in a distributed environment have lagged behind. Data discovery tools and platforms can help.
[link] Alireza Sadeghi: Open Source Data Engineering Landscape 2025 This article comprehensively overviews the 2025 open-source data engineering landscape, highlighting key trends, active projects, and emerging technologies.
In this context, data management in an organization is a key point for the success of its projects involving data. One of the main aspects of correct data management is the definition of a dataarchitecture. What is Delta Lake? The data became useless. The Lakehouse architecture was one of them.
Today, as data sources become increasingly varied, data management becomes more complex, and agility and scalability become essential traits for data leaders, data fabric is quickly becoming the future of dataarchitecture. If data fabric is the future, how can you get your organization up-to-speed?
Today, as data sources become increasingly varied, data management becomes more complex, and agility and scalability become essential traits for data leaders, data fabric is quickly becoming the future of dataarchitecture. If data fabric is the future, how can you get your organization up-to-speed?
In today’s data-driven world, datalakes have emerged as the dataarchitecture of choice when storing and analyzing large volumes of data. However, implementing a successful datalake requires diligent planning and design, as it can quickly become a data swamp with no additional value.
Key Differences Between AI Data Engineers and Traditional Data Engineers While traditional data engineers and AI data engineers have similar responsibilities, they ultimately differ in where they focus their efforts. Data Storage Solutions As we all know, data can be stored in a variety of ways.
In 2010, a transformative concept took root in the realm of data storage and analytics — a datalake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. What is a datalake?
Cloudera customers run some of the biggest datalakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and datalakes.
When I heard the words ‘decentralised dataarchitecture’, I was left utterly confused at first! In my then limited experience as a Data Engineer, I had only come across centralised dataarchitectures and they seemed to be working very well. New data formats emerged — JSON, Avro, Parquet, XML etc.
The Rise of Data Observability Data observability has become increasingly critical as companies seek greater visibility into their data processes. This growing demand has found a natural synergy with the rise of the datalake. As a result, monitoring data in real time was often an afterthought.
The data mesh design pattern breaks giant, monolithic enterprise dataarchitectures into subsystems or domains, each managed by a dedicated team. First-generation – expensive, proprietary enterprise data warehouse and business intelligence platforms maintained by a specialized team drowning in technical debt.
Summary Datalakes have been gaining popularity alongside an increase in their sophistication and usability. Despite improvements in performance and dataarchitecture they still require significant knowledge and experience to deploy and manage. Can you describe what Cuelake is and the story behind it?
Over the past decade, Cloudera has enabled multi-function analytics on datalakes through the introduction of the Hive table format and Hive ACID. Companies, on the other hand, have continued to demand highly scalable and flexible analytic engines and services on the datalake, without vendor lock-in.
Data Gets Meshier. 2022 will bring further momentum behind modular enterprise architectures like data mesh. The data mesh addresses the problems characteristic of large, complex, monolithic dataarchitectures by dividing the system into discrete domains managed by smaller, cross-functional teams.
For instance, analytical data may uncover customer preferences or seasonal product trends, helping teams in strategic planning. This data is commonly gathered through an ETL process and stored in a central location, such as a datalake. Want to see how Striim’s Data Mesh and AI can benefit your organization?
Organizations have begun to built data warehouses and lakes to analyze large amounts of data for insights and business reports. Often time they bring data from multiple data silos into their datalake and also have data stored in particular data stores like NoSQL databases to support different use cases.
Data pipelines are the backbone of your business’s dataarchitecture. Implementing a robust and scalable pipeline ensures you can effectively manage, analyze, and organize your growing data. Understanding the essential components of data pipelines is crucial for designing efficient and effective dataarchitectures.
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