Data Science Blog: Data Engineering

MAY 20, 2024

Enter AnalyticsCreator AnalyticsCreator, a powerful tool for data management, brings a new level of efficiency and reliability to the CI/CD process. It offers full BI-Stack Automation, from source to data warehouse through to frontend. It supports a holistic data model, allowing for rapid prototyping of various models.

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Precisely

JANUARY 25, 2024

There are three potential approaches to mainframe modernization: Data Replication creates a duplicate copy of mainframe data in a cloud data warehouse or data lake, enabling high-performance analytics virtually in real time, without negatively impacting mainframe performance. Read our ebook to learn more.

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Metadata Data Governance Relational Database Government 67

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?

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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.

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Cloudera

FEBRUARY 11, 2019

The root cause is firmly entrenched in legacy systems and traditional data governance challenges that not only result in data silos but also the misguided belief that data privacy is diametrically opposed to effective exploration of information. Governing digital transformation. Governing for compliance.

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Data Engineering Podcast

JUNE 5, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking all of that information into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Go to dataengineeringpodcast.com/atlan today to learn more about how you can take advantage of active metadata and escape the chaos.

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Towards Data Science

JULY 24, 2023

A data engineering manager at a Fortune 500 company expressed the pain of on-prem limitations to me by saying: “Our analysts were unable to run the queries they wanted to run when they wanted to run them. Why are these things related, and more importantly, why should data leaders care? Take data governance for example.

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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?

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Precisely

OCTOBER 16, 2023

The cloud is especially well-suited to large-scale storage and big data analytics, due in part to its capacity to handle intensive computing requirements at scale. BI platforms and data warehouses have been replaced by modern data lakes and cloud analytics solutions. Secure data exchange takes on much greater importance.

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RandomTrees

FEBRUARY 6, 2024

Generative AI with Data Lineage: By automating the process of collecting lineage metadata, generating visualizations of data lineage, and identifying and troubleshooting data lineage problems.

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Data Engineering Podcast

JULY 10, 2022

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. Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code.

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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?

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Ascend.io

APRIL 7, 2023

Data fabric is a centralized platform architecture originating from a curated metadata layer that sits on top of an organization’s data infrastructure. Every time a new data source is added, the metadata layer is updated to define how and when that data should be used. Increasing speed.

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AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

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Monte Carlo

FEBRUARY 26, 2024

A data catalog is a constantly updated inventory of the universe of data assets within an organization. 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.

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Monte Carlo

DECEMBER 8, 2023

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.

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Monte Carlo

DECEMBER 8, 2023

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.

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Monte Carlo

APRIL 24, 2023

Traditionally, after being stored in a data lake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption. Databricks Data Catalog and AWS Lake Formation are examples in this vein. See our post: Data Lakes vs. Data Warehouses.

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Monte Carlo

NOVEMBER 14, 2023

Databricks announced that Delta tables metadata will also be compatible with the Iceberg format, and Snowflake has also been moving aggressively to integrate with Iceberg. It is designed to be easily queryable with SQL even for large analytic tables (we’re talking petabytes of data). How Apache Iceberg tables structure metadata.

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Databand.ai

AUGUST 30, 2023

Data Cataloging Data cataloging is the process of organizing and categorizing data assets to make them easily discoverable and understandable for data teams. This enables data teams to quickly and easily find the data they need for their analytics projects.

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Monte Carlo

AUGUST 19, 2023

By integrating tools from a variety of vendors, a data platform enables a data engineering team to not only manage an organization’s data but also activate it for a domain’s use cases. In today’s data-driven landscape, building a data platform is no longer a nice-to-have, but a necessity for most organizations.

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AltexSoft

NOVEMBER 10, 2021

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? Data warehouse vs data lake vs data lakehouse: What’s the difference.

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Monte Carlo

JULY 18, 2023

A data engineering manager at a Fortune 500 company expressed the pain of on-prem limitations to me by saying: “Our analysts were unable to run the queries they wanted to run when they wanted to run them. Why are these things related, and more importantly, why should data leaders care? Take data governance for example.

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Metadata Data Warehouse Data Lake Government 52

Monte Carlo

JANUARY 23, 2024

“We can see this shift in the rise of data reliability engineering , and data engineering being responsible for managing (not building) data infrastructure and overseeing the performance of cloud-based systems.” Nowadays, Maxime suggests, it’s widely accepted that governance is distributed. But maybe that’s OK.”

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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.

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AltexSoft

AUGUST 29, 2023

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. Data warehouse vs. data lake in a nutshell.

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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?

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AltexSoft

NOVEMBER 23, 2021

Before we get into more detail, let’s determine how data virtualization is different from another, more common data integration technique — data consolidation. Data virtualization vs data consolidation. The example of a typical two-tier architecture with a data lake and data warehouses and several ETL processes.

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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?

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Snowflake

JUNE 27, 2023

Supporting mission criticality with enhanced native data governance, new Snowflake UIs, a growing compliance footprint, and updated cross-cloud business continuity At Snowflake, we’re committed to providing best-in-class native data governance features for customers entrusting our platform with their data.

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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

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Monte Carlo

NOVEMBER 20, 2023

Unlike the varying degrees of manual monitoring we looked at in the previous section, data observability solution Monte Carlo provides automated detection and alerting for data anomalies right out of the box, including automatic thresholding based on metadata and the ability to quickly create deep monitors for known issues and critical SLAs.

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phData: Data Engineering

JULY 18, 2023

Why Migrate to a Modern Data Stack? With the birth of cloud data warehouses, data applications, and generative AI , processing large volumes of data faster and cheaper is more approachable and desired than ever. First up, let’s dive into the foundation of every Modern Data Stack, a cloud-based data warehouse.

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Databand.ai

JULY 28, 2022

Data lineage instills this confidence by providing clear information about data origin and how data has moved and changed since then. Data lineage provides that understanding to support everything from regulatory compliance to risk management around data breaches. This technique focuses directly on the data (vs.

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Metadata Data Lake Datasets Data Warehouse 52

Monte Carlo

APRIL 1, 2021

is whether to choose a data warehouse or lake to power storage and compute for their analytics. is whether to choose a data warehouse or lake to power storage and compute for their analytics. While data warehouses provide structure that makes it easy for data teams to efficiently operationalize data (i.e.,

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Monte Carlo

JULY 18, 2023

A data engineering manager at a Fortune 500 company expressed the pain of on-prem limitations to me by saying: “Our analysts were unable to run the queries they wanted to run when they wanted to run them. Why are these things related, and more importantly, why should data leaders care? Take data governance for example.

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Metadata Data Warehouse Data Lake Government 40

AltexSoft

FEBRUARY 11, 2023

It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);

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