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

Monte Carlo

MARCH 9, 2023

As a result of these challenges and the severe consequences of inaccurate models, the MLOps engineer is an increasingly in demand role for data teams. The 2022 LinkedIn jobs report had “machine learning engineer” as the fourth fastest growing role in the country with nearly 20% of jobs being available for remote work.

Engineering 52

Engineering Data Pipeline Machine Learning Data Science 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

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

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

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. This makes data lakes more adaptable and scalable to handle diverse data types and large volumes of data.

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

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. It’s no fun working in data analytics/science when you are the bottleneck in your company’s business processes. The product includes data and operations.

Pharmaceutical 98

Pharmaceutical Data Lake Data Architecture Scala 98

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.

Data Integration 100

Data Integration Metadata Architecture Media 100

Data Engineering Podcast

OCTOBER 14, 2019

Summary Managing a data warehouse can be challenging, especially when trying to maintain a common set of patterns. They provide an AWS-native, serverless, data infrastructure that installs in your VPC. Datacoral helps data engineers build and manage the flow of data pipelines without having to manage any infrastructure.

Data Warehouse 100

Data Warehouse PostgreSQL AWS Programming Language 100

AltexSoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

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

Knowledge Hut

APRIL 20, 2023

An important aspect of data governance is measuring and monitoring its effectiveness using data governance metrics. The best Data Engineering Bootcamp will help you optimise and analyse data like a pro. What is Data Governance?

Data Governance 59

Data Governance Government Data Cleanse Data Security 59

Cloudera

AUGUST 30, 2022

Modern data architectures. To eliminate or integrate these silos, the public sector needs to adopt robust data management solutions that support modern data architectures (MDAs). Towards Data Science ). Solutions that support MDAs are purpose-built for data collection, processing, and sharing.

Data Architecture 79

Data Architecture Architecture Data Lake NoSQL 79

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

Monte Carlo

MAY 14, 2024

As organizations seek greater value from their data, data architectures 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.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

Data Engineering Podcast

JUNE 17, 2023

By incorporating column-level lineage in the data modeling process it encourages a more robust and well-informed design. In this episode Satish Jayanthi explores the benefits of incorporating column-aware tooling in the data modeling process. Rudderstack]([link] RudderStack provides all your customer data pipelines in one platform.

Data Lake 130

Data Lake Metadata Machine Learning Data Architecture 130

AltexSoft

OCTOBER 30, 2021

Was Nikola Tesla a scientist or engineer? These men didn’t stop at scientific research and ended up conceptualizing or engineering their inventions. Engineers are not only the ones bearing helmets and operating on construction sites. Data science vs data engineering. How about Edison? Or Da Vinci?

Data Engineering 98

Data Engineering Data Engineer Engineering Machine Learning 98

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

ProjectPro

APRIL 20, 2017

Having multiple hadoop projects on your resume will help employers substantiate that you can learn any new big data skills and apply them to real life challenging problems instead of just listing a pile of hadoop certifications. Data Analyst Responsibilities-What does a data analyst do? What is Data Engineering?

Hadoop 40

Hadoop Big Data Coding Project 40

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

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.

Metadata 110

Metadata Datasets BI SQL 110

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. By being a truly open table format, Apache Iceberg fits well within the vision of the Cloudera Data Platform (CDP). What is Apache Iceberg? 2: Open formats.

Metadata 92

Metadata Data Architecture BI Machine Learning 92

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.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Snowflake

DECEMBER 12, 2023

Iceberg Tables bring the easy management and great performance of Snowflake to data stored externally in an open source format. Data Pipelines Improved processing for streaming data with Dynamic Tables – public preview Streaming and CDC data can be challenging to handle. Learn more about Iceberg Tables here.

Metadata 91

Metadata Python AWS Government 91

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.

Engineering 60

Engineering BI Business Analyst Metadata 60

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

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

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

Confluent

MAY 21, 2019

They are at the intersection of the way we develop software, the way we manage data, metadata and the interactions between teams. To illustrate, let’s look at two examples: Documented contracts: Pretend that you are a data scientist working for our imaginary insurance company. They are contract between teams. Couldn’t you tell?”

Kafka 110

Kafka Insurance Architecture Database 110

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. ACID transaction support. Ingestion layer.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

AltexSoft

JULY 18, 2023

These seemingly unrelated terms unite within the sphere of big data, representing a processing engine that is both enduring and powerfully effective — Apache Spark. Maintained by the Apache Software Foundation, Apache Spark is an open-source, unified engine designed for large-scale data analytics.

Big Data 52

Big Data Data Process Process Hadoop 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

JULY 19, 2022

In the last few decades, we’ve seen a lot of architectural approaches to building data pipelines , changing one another and promising better and easier ways of deriving insights from information. There have been relational databases, data warehouses, data lakes, and even a combination of the latter two.

Architecture 52

Architecture Data Lake Medical Datasets 52

ProjectPro

JANUARY 31, 2022

Snowflake provides data warehousing, processing, and analytical solutions that are significantly quicker, simpler to use, and more adaptable than traditional systems. Snowflake is not based on existing database systems or big data software platforms like Hadoop.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

Advancing Analytics: Data Engineering

NOVEMBER 10, 2020

In many pure data warehousing solutions, machine learning is an after-thought, plumbed in via awkward extensions, or even via export/score/re-import workarounds. This new home has the huge benefit of being backed by the full power of the Spark engine and the data availability of the lake.

SQL 52

SQL BI Data Warehouse Data Lake 52

ProjectPro

JANUARY 31, 2023

Big Data Interview Questions and Answers Based on Job Role With the help of ProjectPro experts, we have compiled a list of interview questions on big data based on several job roles, including big data tester, big data developer, big data architect, and big data engineer. What are its benefits?

Big Data 52

Big Data Hadoop AWS Relational Database 52