Monte Carlo

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture?

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Data Engineering Weekly

JANUARY 15, 2023

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make it easy to collect data from every application, website, and SaaS platform, then activate it in your warehouse and business tools. 2023 predictions from the panel are; Unified metadata becomes kingmaker.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Data Engineering Podcast

JULY 3, 2022

She also discusses her views on the role of the data lakehouse as a building block for these architectures and the ongoing influence that it will have as the technology matures. Atlan is the metadata hub for your data ecosystem. Tired of deploying bad data? Need to automate data pipelines with less red tape?

Architecture 100

Architecture Metadata MongoDB MySQL 100

Knowledge Hut

NOVEMBER 28, 2023

The contemporary world experiences a huge growth in cloud implementations, consequently leading to a rise in demand for data engineers and IT professionals who are well-equipped with a wide range of application and process expertise. Data Engineer certification will aid in scaling up you knowledge and learning of data engineering.

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

Towards Data Science

APRIL 6, 2023

Today’s post follows the same philosophy: fitting local and cloud pieces together to build a data pipeline. And, when it comes to data engineering solutions, it’s no different: They have databases, ETL tools, streaming platforms, and so on — a set of tools that makes our life easier (as long as you pay for them).

AWS 77

AWS Data Pipeline Amazon Web Services Python 77

Silectis

JANUARY 3, 2021

If you’re new to data engineering or are a practitioner of a related field, such as data science, or business intelligence, we thought it might be helpful to have a handy list of commonly used terms available for you to get up to speed. Data Engineering Data engineering is a process by which data engineers make data useful.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

AltexSoft

OCTOBER 30, 2021

If you’re an executive who has a hard time understanding the underlying processes of data science and get confused with terminology, keep reading. We will try to answer your questions and explain how two critical data jobs are different and where they overlap. Data science vs data engineering.

Data Engineering 98

Data Engineering Data Engineer Engineering Machine Learning 98

ProjectPro

SEPTEMBER 26, 2021

According to a survey, big data engineering job interviews increased by 40% in 2020 compared to only a 10% rise in Data science job interviews. Table of Contents Big Data Engineer - The Market Demand Who is a Big Data Engineer? Most of these are performed by Data Engineers.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

AltexSoft

FEBRUARY 11, 2023

To get a better understanding of a data architect’s role, let’s clear up what data architecture is. Data architecture is the organization and design of how data is collected, transformed, integrated, stored, and used by a company. What is the main difference between a data architect and a data engineer?

Data Architect 87

Data Architect Certification Generalist Big Data 87

Cloudera

FEBRUARY 22, 2022

Over the past decade, the successful deployment of large scale data platforms at our customers has acted as a big data flywheel driving demand to bring in even more data, apply more sophisticated analytics, and on-board many new data practitioners from business analysts to data scientists.

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Metadata Datasets BI SQL 107

Monte Carlo

NOVEMBER 21, 2022

What’s next for the future of data engineering? Each year, we chat with one of our industry’s pioneering leaders about their predictions for the modern data stack – and share a few of our own. Pro-tip: be sure to check out his talk from IMPACT: The Data Observability Summit. Image courtesy of Tomasz Tunguz.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Snowflake

DECEMBER 12, 2023

To give customers flexibility for how they fit Snowflake into their data architecture, 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.

Metadata 90

Metadata Python AWS Government 90

Databand.ai

JULY 19, 2023

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.

Pipeline-centric 52

Pipeline-centric Data Governance Metadata Government 52

Databand.ai

AUGUST 30, 2023

It encompasses the systems, tools, and processes that enable businesses to manage their data more efficiently and effectively. These systems typically consist of siloed data storage and processing environments, with manual processes and limited collaboration between teams.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Monte Carlo

MARCH 13, 2024

When to implement data governance Data governance is now considered a must-have for most organizations because it helps improve compliance with GDPR and other measures, safeguard proprietary data against bad actors, support a distributed data architecture, and maintain data quality.

Data Governance 52

Data Governance Government Metadata Data 52

Cloudera

MARCH 23, 2022

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.

Metadata 90

Metadata Data Architecture BI Machine Learning 90

Ascend.io

AUGUST 31, 2023

In the dynamic world of data, many professionals are still fixated on traditional patterns of data warehousing and ETL, even while their organizations are migrating to the cloud and adopting cloud-native data services. Shifting between these two patterns usually involves migration to new tooling to acquire data from sources.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Data Engineering Podcast

OCTOBER 28, 2019

In this episode he explains his motivation for creating a product for data management, how the programming model simplifies the work of building testable and maintainable pipelines, and his vision for the future of data programming. If you are building dataflows then Dagster is definitely worth exploring.

Building 100

Building Data Pipeline Programming Language Metadata 100

Monte Carlo

FEBRUARY 9, 2023

Over the past several years, data warehouses have evolved dramatically, but that doesn’t mean the fundamentals underpinning sound data architecture needs to be thrown out the window. Pie Insurance , a leading small business insurtech, leverages a data vault 2.0 How do you implement a data vault architecture?

Architecture 52

Architecture Raw Data Metadata Data Warehouse 52

Data Engineering Podcast

JULY 28, 2019

Rather than manually defining all of the mappings ahead of time, we can rely on the power of graph databases and some strategic metadata to allow connections to occur as the data becomes available. If you are struggling to maintain a tangle of data pipelines then you might find some new ideas for reducing your workload.

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Data Integration Metadata Media Architecture 100

DataKitchen

AUGUST 3, 2021

The data mesh design pattern breaks giant, monolithic enterprise data architectures into subsystems or domains, each managed by a dedicated team. The communication between business units and data professionals is usually incomplete and inconsistent. Introduction to Data Mesh. Source: Thoughtworks.

Pharmaceutical 98

Pharmaceutical Data Lake Data Architecture Scala 98

Data Engineering Podcast

OCTOBER 14, 2019

Dataform is a platform that helps you apply engineering principles to your data transformations and table definitions, including unit testing SQL scripts, defining repeatable pipelines, and adding metadata to your warehouse to improve your team’s communication.

Data Warehouse 100

Data Warehouse PostgreSQL AWS Programming Language 100

Cloudera

OCTOBER 7, 2021

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”.

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Architecture Metadata Government Kafka 124

Monte Carlo

JANUARY 23, 2023

But while most every company would consider themselves a “data-first” organization, not every data architecture 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.

Data Lake 52

Data Lake Unstructured Data Business Intelligence Architecture 52

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.

Metadata 52

Metadata Data Data Warehouse Government 52

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.

Metadata 52

Metadata Data Data Warehouse Government 52

DataKitchen

OCTOBER 11, 2021

First, you must understand the existing challenges of the data team, including the data architecture and end-to-end toolchain. The biggest challenge is broken data pipelines due to highly manual processes. Figure 1 shows a manually executed data analytics pipeline. Monitoring Job Metadata.

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Engineering BI Business Analyst Metadata 60

DataKitchen

AUGUST 8, 2023

In the article, Melody Chien notes that Data Observability is a practice that extends beyond traditional monitoring and detection, providing robust, integrated visibility over data and data landscapes. It alerts data and analytics leaders to issues with their data before they multiply. Are problems with data tests?

Data Pipeline 52

Data Pipeline Data Government Metadata 52

Monte Carlo

MARCH 9, 2023

It is the job of the MLOps engineer to ensure the model performs as well in the real world as it does in the lab, which requires some data science knowledge alongside strong engineering and orchestration skills. JetBlue’s data architecture. But in summary, ML models are only as good as the input they are given.

Engineering 52

Engineering Data Pipeline Machine Learning Data Science 52

Monte Carlo

APRIL 1, 2021

Here’s where data catalogs fall short and how data discovery platforms and tools can help ensure your data lake doesn’t turn into a data swamp. Here’s why some of the best data engineering teams are rethinking their approach to building data catalogs — and what types of data discovery platforms data lakes need instead.

Data Lake 52

Data Lake Unstructured Data Data Warehouse Metadata 52

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? Schema enforcement and data governance. Metadata layer. Metadata layer. Ingestion layer.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

Monte Carlo

MARCH 2, 2023

A data anomaly is when the data is incorrect or missing as a result of bad data or broken data pipelines. Not only are data anomalies painful, they are costly to resolve. That’s 549 engineering hours each month. Data SLAs You can’t improve what you don’t measure.

Food 52

Food Data SQL Data Pipeline 52

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. Watch our video explaining how data engineering works.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Monte Carlo

SEPTEMBER 16, 2020

To keep pace with data’s lightning innovation speed, data engineers need to invest not only in the latest data modeling and analytics tools, but also technologies that can increase data accuracy and prevent broken ETL pipelines. The good news about bad data? The solution?

Software Engineer 52

Software Engineer Software Engineering Data Data Pipeline 52

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Scala 64

Scala Data Lake BI Google Cloud 64

Monte Carlo

JULY 30, 2020

In the age of self-service business intelligence , nearly every company considers themselves a data-first company, but not every company is treating their data architecture with the level of democratization and scalability it deserves. Your company, for one, views data as a driver of innovation.

IT 45

IT Data Lake Business Intelligence Data Architecture 45

Monte Carlo

AUGUST 17, 2021

And your entire data engineering team is all in on this “data as code” movement. The flip side of this data-driven coin: your stakeholders (CEO and CTO included) ping you nearly every other hour to ask you: “is my data up-to-date?” To achieve this data trust, they turned to Monte Carlo.

Insurance 40

Insurance Retail Data Pipeline Portfolio 40