Jesse Anderson

NOVEMBER 15, 2022

My guest this week is Kulani Likotsi , the Head of Data Management and Data Governance at one of the four biggest banks in Africa. She’s had a rising career journey going from an analyst, to a Business Intelligence developer, to the data warehouse team, to the data governance team.

Data Governance 130

Data Governance Government Banking Business Intelligence 130

Knowledge Hut

MARCH 7, 2024

Two, it creates a commonality of data definitions, concepts, metadata and the like. The traditional data management and data warehouses, and the sequence of data transformation, extraction and migration- all arise a situation in which there are risks for data to become unsynchronized.

Big Data 98

Big Data Bytes Data Governance Raw Data 98

Knowledge Hut

MARCH 28, 2024

Role Level Advanced Responsibilities Design and architect data solutions on Azure, considering factors like scalability, reliability, security, and performance. Develop data models, data governance policies, and data integration strategies. Familiarity with ETL tools and techniques for data integration.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Christophe Blefari

SEPTEMBER 15, 2023

How to reduce warehouse costs? — Hugo propose 7 hacks to optimise data warehouse cost. teej/titan — Titan is a Python library to manage data warehouse infrastructure. Titan allows you to create Snowflake Databases, Warehouses, Role and RoleGrant in a programmatic manner. Crazy amounts.

Data Warehouse 130

Data Warehouse Data SQL Python 130

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Does not have the resources to implement robust data governance and management.

Data Management 52

Data Management Data Lake Management Data Warehouse 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Does not have the resources to implement robust data governance and management.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Does not have the resources to implement robust data governance and management.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

Precisely

MAY 13, 2024

Wide support for enterprise-grade sources and targets Large organizations with complex IT landscapes must have the capability to easily connect to a wide variety of data sources. Whether it’s a cloud data warehouse or a mainframe, look for vendors who have a wide range of capabilities that can adapt to your changing needs.

Data Integration 52

Data Integration Metadata Amazon Web Services Data Governance 52

François Nguyen

MARCH 22, 2021

TL;DR After setting up and organizing the teams, we are describing 4 topics to make data mesh a reality. How do we build data products ? How can we interoperate between the data domains ? The data domain Discovery portal with all the metadata on the data life cycle 4.Federated

Architecture 130

Architecture Technology Google Cloud Metadata 130

phData: Data Engineering

SEPTEMBER 19, 2023

Cost reduction by minimizing data redundancy, improving data storage efficiency, and reducing the risk of errors and data-related issues. Data Governance and Security By defining data models, organizations can establish policies, access controls, and security measures to protect sensitive data.

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Towards Data Science

JUNE 8, 2023

This post will focus on the most common team ownership models including: data engineering, data reliability engineering, analytics engineering, data quality analysts, and data governance teams. Why is data quality ownership important? The governance team treats every team output as a data product.

Data Governance 79

Data Governance Government Generalist Data Engineering 79

Monte Carlo

MARCH 25, 2024

.” Justin Swenson Data, Analytics, and AI Governance Lead, Sub-Zero That means Monte Carlo needs to support and integrate across those same solutions and architectures. More pipelines, more data sources, and more consumers. Deepening our integrations with modern orchestration tools like dbt and Airflow.

MySQL 64

MySQL Government Data Governance Kafka 64

Data Engineering Podcast

JULY 12, 2021

RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. RudderStack’s smart customer data pipeline is warehouse-first.

Designing 100

Designing Data Warehouse Data Governance Consulting 100

Monte Carlo

JUNE 6, 2023

This post will focus on the most common team ownership models including: data engineering, data reliability engineering, analytics engineering, data quality analysts, and data governance teams. Table of Contents Why is important to answer who is responsible for data quality?

Data Governance 98

Data Governance Government Data Data Engineering 98

Data Engineering Podcast

APRIL 26, 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. RudderStack’s smart customer data pipeline is warehouse-first.

Business Intelligence 100

Business Intelligence Data Warehouse Hadoop Data Pipeline 100

Databand.ai

JULY 19, 2023

Data lineage tools are not a new concept. However, their importance has grown significantly in recent years due to the increasing complexity of data architectures and the growing need for data governance and compliance. In this article: Why Are Data Lineage Tools Important?

Pipeline-centric 52

Pipeline-centric Data Governance Metadata Government 52

Snowflake

NOVEMBER 14, 2023

Several outcomes factored into Snowflake’s performance rating: Among the top customer outcomes and benefits reported by respondents are improved operational efficiency (88%) and cost-effectiveness (38%), followed by improved data integration and decision-making capabilities.

Healthcare 84

Healthcare Data Warehouse Data Governance Cloud 84

Data Engineering Podcast

JUNE 5, 2022

Datafold shows how a change in SQL code affects your data, both on a statistical level and down to individual rows and values before it gets merged to production. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows.

Data Security 100

Data Security Metadata MongoDB Scala 100

Data Engineering Podcast

APRIL 5, 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. RudderStack’s smart customer data pipeline is warehouse-first.

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 100

Christophe Blefari

JUNE 16, 2023

Why data consumers do not trust your reporting — It is a good illustration of the data journey manifesto. Stakeholders often notice data issues before the data team does. Data warehouses are mutable, this is one of the many root causes proposed by Lucas. Data Documentation 101: Why?

Programming Language 130

Programming Language SQL PostgreSQL Data 130

Data Engineering Podcast

OCTOBER 15, 2021

Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. No more scripts, just SQL.

Metadata 100

Metadata BI Data Warehouse Government 100

RandomTrees

FEBRUARY 29, 2024

Snowflake is a cloud-based data warehousing platform that has arisen as a primary answer for organizations hoping to use the power of data analytics in the cloud. With its adaptability, execution, and usability, Snowflake offers critical benefits over conventional on-premises data warehouses.

Data Warehouse 52

Data Warehouse Utilities Consulting Certification 52

Data Engineering Weekly

NOVEMBER 26, 2023

We saw a fleet of announcements from Data Catalogs tools on how LLM can help to auto-generate documentation [See: How Generative AI Is Making Data Catalogs Smarter ]. I believe the impact of LLM will go further down in the stack with data storage formats in the coming years.

Data Engineering 70

Data Engineering Data Engineer Engineering Kafka 70

Snowflake

FEBRUARY 27, 2023

Data, a driving force for business performance In light of such massive growth, data management has steadily become more complex, to the point of introducing tangible risks. ” The migration from the existing data warehouse to the Snowflake platform took six months, with both being run in parallel during the last month.

Cloud 52

Cloud Retail Data Warehouse Data 52

Data Engineering Podcast

SEPTEMBER 23, 2018

This includes modeling the lifecycle of your information as a pipeline from the raw, messy, loosely structured records in your data lake, through a series of transformations and ultimately to your data warehouse. What is your opinion on the relative merits of a data warehouse vs a data lake and are they mutually exclusive?

Data Lake 100

Data Lake Data Warehouse Data Architecture Architecture 100

Cloudera

JANUARY 28, 2022

Then there are the more extensive discussions – scrutiny of the overarching, data strategy questions related to privacy, security, data governance /access and regulatory oversight. These are not straightforward decisions, especially when data breaches always hit the top of the news headlines.

Cloud 115

Cloud Banking Data Governance Government 115

Monte Carlo

JULY 27, 2023

Data quality engineers also need to have experience operating in cloud environments and using many of the modern data stack tools that are utilized in building and maintaining data pipelines. 78% of job postings referenced at least part of their environment was in a modern data warehouse, lake, or lakehouse.

Engineering 52

Engineering Healthcare Scala Data Warehouse 52

Monte Carlo

MAY 18, 2023

Whether you’re a data scientist, data engineer, or business analyst, keeping track of your data’s origin, transformation, and movement is crucial for maintaining transparency, enforcing data governance, and ensuring data quality. Why do you need data lineage?

Metadata 64

Metadata Data Lake Government Data Governance 64

Monte Carlo

SEPTEMBER 12, 2022

But with the rise of tools such as Segment, Fivetran, Meltano, and Airbyte, it’s become relatively easy for teams to bring all of their data from external sources into a centralized place like a data warehouse. Maxime points to the decentralization of data governance as a possible solution. “As

BI 52

BI Data Governance ETL Tools Data Warehouse 52

Cloudera

NOVEMBER 12, 2020

Your sunk costs are minimal and if a workload or project you are supporting becomes irrelevant, you can quickly spin down your cloud data warehouses and not be “stuck” with unused infrastructure. Cloud deployments for suitable workloads gives you the agility to keep pace with rapidly changing business and data needs.

Cloud 103

Cloud Data Warehouse Banking Data 103

Data Engineering Podcast

JULY 10, 2022

Datafold shows how a change in SQL code affects your data, both on a statistical level and down to individual rows and values before it gets merged to production. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows.

Metadata 100

Metadata MongoDB MySQL Data Pipeline 100

Lyft Engineering

OCTOBER 3, 2023

In this post we will define data quality at a high-level and explore our motivation to achieve better data quality. We will then introduce our in-house product, Verity, and showcase how it serves as a central platform for ensuring data quality in our Hive Data Warehouse. What and Where is Data Quality?

Big Data 84

Big Data Metadata Data Warehouse Data 84

dbt Developer Hub

JULY 2, 2023

The Data Vault data model is designed using a few well-applied principles, and in practice, pools source data so it is available for use by all downstream consumers. This promotes a scalable data warehouse through reusability and modularity. Should you consider Data Vault for your data warehouse?

Cloud 52

Cloud Data Warehouse Data BI 52

Cloudera

NOVEMBER 1, 2022

Through modern data architectures powered by CDP, including Cloudera-enabled data fabric, data lakehouse, and data mesh , DoD agencies can rapidly provision and manage innovative data engineering, data warehouse, and machine learning environments, with access to secured supply chain data stored in CDP Private Cloud.

Cloud 89

Cloud Government Cloud Computing Data Architecture 89

Databand.ai

JULY 19, 2023

This step is vital as it ensures that the data is usable and can provide valuable insights when analyzed. Data Loading Data loading is where the transformed data is loaded into a data warehouse or any other desired destination for further analysis or reporting.

Data Ingestion 52

Data Ingestion Process Data Cleanse Data Governance 52

Snowflake

FEBRUARY 1, 2023

Prior to implementing Snowflake in 2021, Western Union’s data spanned 34 on-premises data warehouses, resulting in latency and operational burdens. Western Union has gained a comprehensive view of the customer while running the Data Cloud on AWS. Does it offer a more seamless customer experience?

Cloud 52

Cloud Data Warehouse Retail Data 52

Monte Carlo

SEPTEMBER 20, 2022

The complexity of Prefect’s data pipelines underscores the value—and challenges—of achieving data reliability at scale. But a growing company means growing data needs. After some initial cheerleading, Dylan convinced the Prefect team to stand up a Monte Carlo instance for their BigQuery data warehouse.

Big Data 52

Big Data Data Warehouse Data Data Governance 52