Monte Carlo

JANUARY 5, 2024

In this article, we’ll peel back the 5 layers that make up data lakehouse architecture: data ingestion, data storage, metadata, API, and data consumption, understand the expanded opportunities a data lakehouse opens up for generative AI, and how to maintain data quality throughout the pipeline with data observability. Metadata layer 4.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Towards Data Science

DECEMBER 1, 2023

However, we will pay particular attention to Access Control (this will be used for dbt access). After the initial sync, you can access your data from the Snowflake UI. As a best practice, you should use different warehouses for different workloads (ad-hoc, sync, BI analytics) or different environments (dev, prod).

Data Engineering 73

Data Engineering Data Engineer Project Engineering 73

Precisely

MAY 31, 2023

In the same way that big cloud-platform providers offer simplified access to infrastructure, and data cloud providers like Databricks and Snowflake have vastly simplified access to data and analytics, modern data integrity tools must streamline and automate data integrity processes.

Data Integration 52

Data Integration Metadata Data Governance Data 52

Snowflake

MAY 8, 2023

By leveraging Snowflake’s comprehensive metadata on query history and operator statistics , alongside Acceldata’s advanced query fingerprinting techniques, users can attain a deeper comprehension of their workloads and pinpoint the most resource-intensive or unoptimized queries. This retrieves the stats of the query ID in question.

Technology 111

Technology SQL BI Utilities 111

Monte Carlo

APRIL 27, 2023

The cloud native no-code analytics and business intelligence (BI) platform is a favorite of data teams for its familiar spreadsheet interface and ability to give business users instant access to explore and get insights. However, the insights within any BI tool are only actionable if they can be accessed and trusted.

BI 52

BI Business Intelligence Metadata Data Warehouse 52

AltexSoft

FEBRUARY 11, 2023

This suggests that today, there are many companies that face the need to make their data easily accessible, cleaned up, and regularly updated. Sample of a high-level data architecture blueprint for Azure BI programs. What’s more, investing in data products, as well as in AI and machine learning was clearly indicated as a priority.

Data Architect 87

Data Architect Certification Generalist Big Data 87

dbt Developer Hub

DECEMBER 11, 2023

The Semantic Layer lets teams define business metrics centrally, in dbt, and access them in multiple analytics tools through our semantic layer APIs. They contain metadata about your dbt models in a format that MetricFlow, the query builder that powers the semantic layer, can understand.

Finance 64

Finance BI SQL Cloud 64

dbt Developer Hub

JULY 25, 2022

If you’ve needed to grant access to a dbt model between 2019 and today, there’s a good chance you’ve come across the "The exact grant statements we use in a dbt project" post on Discourse. Option B: Too clever by half ​ Now that grants is a real config in dbt, available via dbt metadata, you can do all sorts of fun things with it.

BI 52

BI Metadata Database SQL 52

Netflix Tech

NOVEMBER 13, 2023

The detailed temporal metadata SMAD provides about speech and music regions in a polyphonic audio mixture are a first step for structural audio segmentation, indexing and pre-processing audio for the following downstream tasks. Likewise, authoring accessibility-features like Audio Description (AD) involves music and speech segmentation.

Datasets 89

Datasets Metadata Algorithm Architecture 89

Monte Carlo

FEBRUARY 21, 2023

Part of the beauty of the data fabric is its ease of use and accessibility for multiple constituents within an organization, including business users who are not a part of the data engineering team. As your team builds your data fabric, make sure you have a designated way to collect the various metadata associated with your data inputs.

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

Monte Carlo

FEBRUARY 21, 2023

Part of the beauty of the data fabric is its ease of use and accessibility for multiple constituents within an organization, including business users who are not a part of the data engineering team. As your team builds your data fabric, make sure you have a designated way to collect the various metadata associated with your data inputs.

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

Monte Carlo

SEPTEMBER 12, 2022

This new approach empowers business users to access and act on their data, while reinforcing the data warehouse as the source of truth. Compared to software engineering, Maxime says, “I think the on-ramp for people to become more sophisticated with data is just easier and more accessible to more people.”

BI 52

BI Data Governance ETL Tools Data Warehouse 52

Data Engineering Podcast

NOVEMBER 9, 2020

He also shares the internal architecture, how he approached the design to make it accessible and easy to use, and how it autodiscovers the schemas and metadata for your source systems. Are you bogged down by having to manually manage data access controls, repeatedly move and copy data, and create audit reports to prove compliance?

Building 100

Building PostgreSQL BI Metadata 100

Data Engineering Podcast

MARCH 22, 2021

Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. e.g. APIs and third party data sources How can we integrage CDC into metadata/lineage tooling?

Data Warehouse 100

Data Warehouse Metadata Data Lake Hadoop 100

AltexSoft

AUGUST 29, 2023

Such an object storage model allows metadata tagging and incorporating unique identifiers, streamlining data retrieval and enhancing performance. Data storage and processing The data storage and processing layer is where the ingested data resides and undergoes transformations to make it more accessible and valuable for analysis.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Data Engineering Podcast

MAY 3, 2021

They explain how to think about your data systems in a holistic and maintainable fashion, the security challenges that threaten to derail your efforts, and the power of using metadata as the foundation of everything that you do. What are some of the things that DataOps isn’t (despite what marketers might say)?

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 100

Monte Carlo

JUNE 28, 2022

By design, data was less structured with limited metadata and no ACID properties. Unity Catalog The Unity Catalog unifies metastores, catalogs, and metadata within Databricks. The Databricks Unity Catalog does what it says: it unifies individual metastores, catalogs and serves as a central metadata repository in Databricks.

Data Lake 52

Data Lake Metadata AWS Data Warehouse 52

Towards Data Science

JUNE 16, 2023

Identifying your business-critical dashboards Looker exposes metadata about content usage in pre-built Explores that you can enrich with your own data to make it more useful. yml file In BI tools, one option that makes it transparent to everyone is to label the title of a dashboard with e.g. “ Tier 1 ” to indicate that it’s critical.

BI 79

BI Data ETL Tools Machine Learning 79

Cloudera

AUGUST 31, 2021

While this “data tsunami” may pose a new set of challenges, it also opens up opportunities for a wide variety of high value business intelligence (BI) and other analytics use cases that most companies are eager to deploy. . Consideration of both data & metadata in the migration. All rights reserved. Value Achieved.

Data Warehouse 80

Data Warehouse Database-centric Metadata Cloud 80

ProjectPro

FEBRUARY 16, 2023

Security AWS offers various security features companies can use to protect their data, such as encryption, access controls, and network isolation. AWS Glue has a central metadata repository called the Glue catalog. The crawler also helps detect any changes in schema and updates metadata as required.

AWS 52

AWS Data Management ETL Tools Management 52

Cloudera

FEBRUARY 9, 2021

In terms of data analysis, as soon as the front-end visualization or BI tool starts accessing the data, the CDW Hive virtual warehouse will spin up cloud computing resources to combine the persisted historical data from the cloud storage with the latest incremental data from Kafka into a transparent real-time view for the users.

Data Warehouse 69

Data Warehouse Cloud Kafka Cloud Storage 69

Silectis

JANUARY 3, 2021

Advanced Analytics The process of discovering deeper insights in data than typically enabled by most business intelligence (BI) tools. BI (Business Intelligence) Strategies and systems used by enterprises to conduct data analysis and make pertinent business decisions. Big Data Large volumes of structured or unstructured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Snowflake

APRIL 5, 2023

Business Intelligence (BI) tools are compatible with dimensional models and help users to create a visualization of data and reports. Metadata in the Data Vault approach helps to track the origin and processing of data. Snowflake’s advantages include columnar storage, micro-partitioning, enhanced data sharing, and data protection.

Data Warehouse 52

Data Warehouse Healthcare Unstructured Data Metadata 52

Hepta Analytics

FEBRUARY 14, 2022

The BI team wants to do analysis of the data. The data is easily accessible and is easy to update. The transformation step takes place in a separate engine and the transformed data is stored in a data store. The process is slow since the data has to be transformed first. The purpose of the data is not determined.

Data Ingestion 52

Data Ingestion Data Engineering Data Engineer Engineering 52

dbt Developer Hub

JULY 2, 2023

Agile development ​ dbt Cloud includes built-in Git with accessible features directly from its IDE, which simplifies development immensely. Apart from data modelling work, you need to agree on naming conventions, hash algorithm, staging strategy, and data types for standard metadata attributes, and make sure these are all well documented.

Cloud 52

Cloud Data Warehouse Data BI 52

Monte Carlo

APRIL 7, 2021

Make sure everyone (data engineers, data analysts, analytics engineers, and data scientists) troubleshooting data problems have access to the most up-to-date lineage. Your data lineage should include data products like BI reports, ML models or reverse ETL sinks to be useful. Field-level lineage is a plus. Image courtesy of Monte Carlo.

Data Engineering 40

Data Engineering Data Engineer Engineering Datasets 40

Cloudera

AUGUST 30, 2018

Cloud service architecture – storing and accessing data and services over the Internet instead of your data center or local computer’s hard drive. How do I meet a growing demand for self-serve BI, while not exploding my data warehouse budgets? How do I get to the next level in the data-driven journey fast enough?

Unstructured Data 40

Unstructured Data Machine Learning Data Warehouse BI 40

Monte Carlo

MARCH 16, 2023

Today, nearly a decade later, Randy leads over 20 people on the Best Egg data and BI teams. The analytics users across the company access most data through Snowflake and PowerBI. They hope playing “March Metadata Madness” will get people used to going to the shared data catalog tool.

Software Engineer 52

Software Engineer Software Engineering Data Governance Government 52

Monte Carlo

JULY 10, 2023

For example, let’s say a streaming provider has 10 million overall subscribers who can access its content. Data profiling Data profiling uses a tool or programming language to surface metadata about your data. Accuracy reflects the degree to which the data correctly describes the “real-world” objects being described.

Data Collection 40

Data Collection Data Governance Government Data 40

Monte Carlo

MARCH 16, 2023

Today, nearly a decade later, Randy leads over 20 people on the Best Egg data and BI teams. The analytics users across the company access most data through Snowflake and PowerBI. They hope playing “March Metadata Madness” will get people used to going to the shared data catalog tool.

Software Engineer 40

Software Engineer Software Engineering Data Governance Government 40

Monte Carlo

APRIL 16, 2022

There is a place for data catalogs as a pre-data warehouse definition surface, but the challenge is there is no carrot and no stick for data consumers to keep metadata current. The data is piped into the modern data warehouse, and with the modeling metadata can ideally be automatically JOINed and categorized.

Data Warehouse 52

Data Warehouse Architecture Data Government 52

Data Engineering Podcast

NOVEMBER 26, 2023

Summary Building a data platform that is enjoyable and accessible for all of its end users is a substantial challenge. Support Data Engineering Podcast Summary Building a data platform that is enjoyable and accessible for all of its end users is a substantial challenge. Go to [materialize.com]([link] today and get 2 weeks free!

Architecture 147

Architecture Data Lake High Quality Data SQL 147

Monte Carlo

FEBRUARY 26, 2024

It uses metadata to create a picture of the data, as well as the relationships between data assets of diverse sources, and the processing that takes place as data moves through systems. Additionally, data catalogs can centrally enforce rules of access, security, and governance, all in a way that is both automatic and visible.

Metadata 52

Metadata Government Data Data Governance 52

Cloudera

MARCH 23, 2022

Apache Iceberg is a high-performance, open table format, born-in-the cloud that scales to petabytes independent of the underlying storage layer and the access engine layer. For query planning Iceberg relies on metadata files, as mentioned earlier, that contains where the data lives and how partitioning and schema are spread across the files.

Metadata 88

Metadata Data Architecture BI Machine Learning 88

Cloudera

FEBRUARY 22, 2022

Through our contributions, we have extended support for Hive and Impala, delivering on the vision of a data architecture for multi-function analytics from large scale data engineering (DE) workloads to fast BI and querying (within DW) and machine learning (ML). It removes the load from the Metastore and Metastore backend database. .

Metadata 105

Metadata Datasets BI SQL 105

Data Engineering Podcast

NOVEMBER 27, 2022

This has led to inefficiencies in how data is stored, accessed, and shared across process and system boundaries. Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Stale dashboards?

Data Process 100

Data Process Process Metadata Business Intelligence 100

Cloudera

AUGUST 30, 2022

But while state and local governments seek to improve policies, decision making, and the services constituents rely upon, data silos create accessibility and sharing challenges that hinder public sector agencies from transforming their data into a strategic asset and leveraging it for the common good. . Forrester ).

Data Architecture 76

Data Architecture Architecture Data Lake NoSQL 76