This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
As data management grows increasingly complex, you need modern solutions that allow you to integrate and access your data seamlessly. Data mesh and data fabric are two modern dataarchitectures that serve to enable better data flow, faster decision-making, and more agile operations.
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.
Key Takeaways: Interest in datagovernance is on the rise 71% of organizations report that their organization has a datagovernance program, compared to 60% in 2023. Datagovernance is a top data integrity challenge, cited by 54% of organizations second only to data quality (56%).
In an effort to better understand where datagovernance is heading, we spoke with top executives from IT, healthcare, and finance to hear their thoughts on the biggest trends, key challenges, and what insights they would recommend. Get the Trendbook What is the Impact of DataGovernance on GenAI?
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.
Your host is Tobias Macey and today I'm interviewing Kevin Liu about his use of Trino and Iceberg for Stripe's data lakehouse Interview Introduction How did you get involved in the area of data management? Can you describe what role Trino and Iceberg play in Stripe's dataarchitecture?
To improve the way they model and manage risk, institutions must modernize their data management and datagovernance practices. Implementing a modern dataarchitecture makes it possible for financial institutions to break down legacy data silos, simplifying data management, governance, and integration — and driving down costs.
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.
Datagovernance refers to the set of policies, procedures, mix of people and standards that organisations put in place to manage their data assets. It involves establishing a framework for data management that ensures data quality, privacy, security, and compliance with regulatory requirements.
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?
Those decentralization efforts appeared under different monikers through time, e.g., data marts versus data warehousing implementations (a popular architectural debate in the era of structured data) then enterprise-wide data lakes versus smaller, typically BU-Specific, “data ponds”.
Can you walk through the stages of an ideal lifecycle for data within the context of an organizations uses for it? What are some of the common mistakes that are made when designing a dataarchitecture and how do they lead to failure?
from China to the UK , new datagovernance and protection rules are coming in on an almost daily basis. But increasingly at Cloudera, our clients are looking for a hybrid cloud architecture in order to manage compliance requirements.
As the amount of enterprise data continues to surge, businesses are increasingly recognizing the importance of datagovernance — the framework for managing an organization’s data assets for accuracy, consistency, security, and effective use. Projections show that the datagovernance market will expand from $1.81
In fact, we recently announced the integration with our cloud ecosystem bringing the benefits of Iceberg to enterprises as they make their journey to the public cloud, and as they adopt more converged architectures like the Lakehouse. 1: Multi-function analytics . 3: Open Performance.
To give customers flexibility for how they fit Snowflake into their dataarchitecture, Iceberg Tables can be configured to use either Snowflake or an external service such as AWS Glue as the table’s catalog to track metadata, with an easy, one-line SQL command to convert the table’s catalog to Snowflake in a metadata-only operation.
This specialist works closely with people on both business and IT sides of a company to understand the current needs of the stakeholders and help them unlock the full potential of data. To get a better understanding of a data architect’s role, let’s clear up what dataarchitecture is.
Key Takeaways Data Fabric is a modern dataarchitecture that facilitates seamless data access, sharing, and management across an organization. Data management recommendations and data products emerge dynamically from the fabric through automation, activation, and AI/ML analysis of metadata.
As organizations seek greater value from their data, dataarchitectures are evolving to meet the demand — and table formats are no exception. At its core, a table format is a sophisticated metadata layer that defines, organizes, and interprets multiple underlying data files.
This capability is useful for businesses, as it provides a clear and comprehensive view of their data’s history and transformations. Data lineage tools are not a new concept. In this article: Why Are Data Lineage Tools Important? It provides context for data, making it easier to understand and manage.
Grab’s Metasense , Uber’s DataK9 , and Meta’s classification systems use AI to automatically categorize vast data sets, reducing manual efforts and improving accuracy. Beyond classification, organizations now use AI for automated metadata generation and data lineage tracking, creating more intelligent data infrastructures.
The consumption of the data should be supported through an elastic delivery layer that aligns with demand, but also provides the flexibility to present the data in a physical format that aligns with the analytic application, ranging from the more traditional data warehouse view to a graph view in support of relationship analysis.
A DataOps architecture is the structural foundation that supports the implementation of DataOps principles within an organization. It encompasses the systems, tools, and processes that enable businesses to manage their data more efficiently and effectively. As a result, they can be slow, inefficient, and prone to errors.
Let’s dig in and explore the landscape of the top so-called “data quality tools” — what they are, what they’re not, and whether they’re the right first step towards more reliable data. Governance helps companies set important standards and achieve higher levels of data security, data accessibility, and data quality.
That’s why we’re excited to announce that uniting diverse data team personas to collectively ensure data quality just got easier, thanks to a new integration between Monte Carlo’s data observability platform and Atlan’s active metadata platform.
Data Catalogs Can Drown in a Data Lake Although exceptionally flexible and scalable, data lakes lack the organization necessary to facilitate proper metadata management and datagovernance. Data discovery tools and platforms can help. Image courtesy of Adrian on Unsplash.
The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. What is a data lakehouse? Schema enforcement and datagovernance. Metadata layer. Metadata layer.
But while most every company would consider themselves a “data-first” organization, not every dataarchitecture is treated to the same level of democratization and scalability. In this post we’ll look at the dizzyingly buzzy data mesh and how it stacks up to the more traditional aggregated architectural approach of a data lake.
Enter: data provenance and data lineage. Data lineage is a visual tool that tracks the movement and transformations of data through various systems, processes, and applications. Data provenance is the record of metadata from data’s original sources, providing the historical context and authenticity of data.
Enter: data provenance and data lineage. Data lineage is a visual tool that tracks the movement and transformations of data through various systems, processes, and applications. Data provenance is the record of metadata from data’s original sources, providing the historical context and authenticity of data.
The governance aspect is perhaps even more important and businesses need to be able to understand where the data comes from. Data lineage, personally identifiable information or PPI and metadata all fall under a broad datagovernance banner which is critically important in terms of what needs to be protected and mapped out.
For today’s Chief Data Officers (CDOs) and data teams, the struggle is real. We’re drowning in data yet thirsting for actionable insights. We need a new approach, a paradigm shift that delivers data with the agility and efficiency of a speedboat – enter Data Products.
In the age of self-service business intelligence , nearly every company considers themselves a data-first company, but not every company is treating their dataarchitecture with the level of democratization and scalability it deserves. Your company, for one, views data as a driver of innovation.
Identify the data sources, types of analytics to be performed, and the expected outcomes to guide your efforts. Build dataarchitecture. To effectively leverage unstructured data, allocate resources toward creating a comprehensive dataarchitecture that supports the storage, management, and analysis of various data types.
Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture. This structure is made efficient by data engineering practices that include object storage. Watch our video explaining how data engineering works.
We’re increasingly coming to realize that the rigid, monolithic architectures we’re currently using just don’t make the cut to store, organize, access, and use the ever-growing amounts of data. Thus, the data mesh follows the seams of organizational units. So, what’s the solution?
We’re increasingly coming to realize that the rigid, monolithic architectures we’re currently using just don’t make the cut to store, organize, access, and use the ever-growing amounts of data. Thus, the data mesh follows the seams of organizational units. So, what’s the solution?
Source: The Data Team’s Guide to the Databricks Lakehouse Platform Integrating with Apache Spark and other analytics engines, Delta Lake supports both batch and stream data processing. Besides that, it’s fully compatible with various data ingestion and ETL tools. Databricks focuses on data engineering and data science.
Better Transparency: There’s more clarity about where data is coming from, where it’s going, why it’s being transformed, and how it’s being used. Improved DataGovernance: This level of transparency can also enhance datagovernance and control mechanisms in the new data system.
I can surface ownership metadata and alert the relevant owners to make sure the appropriate changes are made so these breakages never happen. Data System Modernization And Team Reorganization The only constant in data engineering is change.
Several organizations can quickly transform, integrate, and analyze their data with Snowflake's Data Cloud. They can also design and run data apps and securely share, gather, and commercialize real-time data. The query processing layer is separated from the disk storage layer in the Snowflake dataarchitecture.
It enables advanced analytics, makes debugging your marketing automations easier, provides natural audit trails for compliance, and allows for flexible, evolving customer data models. So next time you’re designing your customer dataarchitecture in your CDP, don’t just think about the current state of your customers.
Data observability platforms reduce your data downtime up to 80% and make your data engineers 30% more time efficient by replacing static, cumbersome data testing with machine learning models that can help detect, resolve, and prevent data issues. Now Go Build Some Data Pipelines!
We organize all of the trending information in your field so you don't have to. Join 37,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content