Data Engineering Weekly

SEPTEMBER 18, 2024

The Rise of Data Observability Data observability has become increasingly critical as companies seek greater visibility into their data processes. This growing demand has found a natural synergy with the rise of the data lake. As a result, monitoring data in real time was often an afterthought.

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Cloudera

AUGUST 13, 2021

Every enterprise is trying to collect and analyze data to get better insights into their business. Whether it is consuming log files, sensor metrics, and other unstructured data, most enterprises manage and deliver data to the data lake and leverage various applications like ETL tools, search engines, and databases for analysis.

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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. What is a data lake?

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Data Lake Architecture IT Amazon Web Services 52

Monte Carlo

OCTOBER 31, 2024

AI data engineers are data engineers that are responsible for developing and managing data pipelines that support AI and GenAI data products. Essential Skills for AI Data Engineers Expertise in Data Pipelines and ETL Processes A foundational skill for data engineers?

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ProjectPro

DECEMBER 7, 2021

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. Table of Contents What is a Data Pipeline? The Importance of a Data Pipeline What is an ETL Data Pipeline?

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Monte Carlo

APRIL 1, 2021

Over the past few years, data lakes have emerged as a must-have for the modern data stack. But while the technologies powering our access and analysis of data have matured, the mechanics behind understanding this data in a distributed environment have lagged behind. Data discovery tools and platforms can help.

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Monte Carlo

MAY 31, 2022

When a data pipeline breaks, data engineers need to immediately understand where the rupture occurred and what has been impacted. Data downtime is costly. Field-level data lineage (not necessarily Spark lineage) with hundreds of connections between objects in upstream and downstream tables.

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ProjectPro

AUGUST 11, 2021

“Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Data Warehouse Architecture What is a Data lake? What is a Data lake?

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Towards Data Science

DECEMBER 4, 2023

A robust data infrastructure is a must-have to compete in the F1 business. We’ll build a data architecture to support our racing team starting from the three canonical layers : Data Lake, Data Warehouse, and Data Mart. in alphabetical order: Apache Airflow, Azure Data Factory, DBT, Google DataForm, …).

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Data Engineer Data Engineering Engineering Data Lake 81

Cloudera

NOVEMBER 15, 2024

Unstructured data not ready for analysis: Even when defenders finally collect log data, it’s rarely in a format that’s ready for analysis. Cyber logs are often unstructured or semi-structured, making it difficult to derive insights from them.

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Metadata Unstructured Data Data Lake Government 76

Ascend.io

AUGUST 31, 2023

Secondly , the rise of data lakes that catalyzed the transition from ELT to ELT and paved the way for niche paradigms such as Reverse ETL and Zero-ETL. Still, these methods have been overshadowed by EtLT — the predominant approach reshaping today’s data landscape. Let’s take a closer look.

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Data Engineering Podcast

NOVEMBER 27, 2022

Data engineers struggling with unreliable data need look no further than Monte Carlo, the leading end-to-end Data Observability Platform! Trusted by the data teams at Fox, JetBlue, and PagerDuty, Monte Carlo solves the costly problem of broken data pipelines. images, documents, etc.)

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Data Process Process Metadata Business Intelligence 100

Cloudera

JUNE 9, 2022

From origin through all points of consumption both on-prem and in the cloud, all data flows need to be controlled in a simple, secure, universal, scalable, and cost-effective way. controlling distribution while also allowing the freedom and flexibility to deliver the data to different services is more critical than ever. .

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Data Collection Data Lake Unstructured Data Retail 93

Cloudera

NOVEMBER 15, 2024

Unstructured data not ready for analysis: Even when defenders finally collect log data, it’s rarely in a format that’s ready for analysis. Cyber logs are often unstructured or semi-structured, making it difficult to derive insights from them.

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Metadata Unstructured Data Data Lake Government 52

Data Engineering Weekly

MARCH 3, 2024

link] Zendesk: dbt at Zendesk The Zendesk team shares their journey of migrating legacy data pipelines to dbt, focusing on making them more reliable, efficient, and scalable. The article also highlights sink-specific improvements and operator-specific enhancements that contribute to the overall performance boost.

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Towards Data Science

MAY 3, 2023

VDK helps you easily perform complex operations, such as data ingestion and processing from different sources, using SQL or Python. You can use VDK to build data lakes and ingest raw data extracted from different sources, including structured, semi-structured, and unstructured data.

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DareData

JANUARY 30, 2023

In this post, we'll discuss some key data engineering concepts that data scientists should be familiar with, in order to be more effective in their roles. These concepts include concepts like data pipelines, data storage and retrieval, data orchestrators or infrastructure-as-code.

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Monte Carlo

MAY 14, 2024

Depending on the quantity of data flowing through an organization’s pipeline — or the format the data typically takes — the right modern table format can help to make workflows more efficient, increase access, extend functionality, and even offer new opportunities to activate your unstructured data.

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Monte Carlo

AUGUST 31, 2023

As the data analyst or engineer responsible for managing this data and making it usable, accessible, and trustworthy, rarely a day goes by without having to field some request from your stakeholders. But what happens when the data is wrong? In our opinion, data quality frequently gets a bad rep.

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Knowledge Hut

SEPTEMBER 26, 2023

A person who designs and implements data management , monitoring, security, and privacy utilizing the entire suite of Azure data services to meet an organization's business needs is known as an Azure Data Engineer. The main exam for the Azure data engineer path is DP 203 learning path.

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Towards Data Science

FEBRUARY 9, 2024

As a result, here’s a short list of what inspired me to write an amendment to my original 2021 article : Scale Companies, big and small, are starting to reach levels of data scale previously reserved for Netflix, Uber, Spotify and other giants creating unique services with data.

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Monte Carlo

JUNE 26, 2023

Over the past decade, Databricks and Apache Spark™ not only revolutionized how organizations store and process their data, but they also expanded what’s possible for data teams by operationalizing data lakes at an unprecedented scale across nearly infinite use cases. billion in 2020 to $17.6

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Knowledge Hut

MARCH 28, 2024

They use many data storage, computation, and analytics technologies to develop scalable and robust data pipelines. Role Level Intermediate Responsibilities Design and develop data pipelines to ingest, process, and transform data.

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Monte Carlo

JUNE 28, 2022

To help organizations realize the full potential of their data lake and lakehouse investments, Monte Carlo, the data observability leader, is proud to announce integrations with Delta Lake and Databricks’ Unity Catalog for full data observability coverage. billion in 2020 to 17.60 billion in 2020 to 17.60

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Data Lake Metadata AWS Data Warehouse 52

Monte Carlo

MARCH 28, 2024

In a nutshell, DataOps engineers are responsible not only for designing and building data pipelines, but iterating on them via automation and collaboration as well. BONUS INTERVIEW QUESTION How would you tackle a data pipeline performance problem? Reliable data starts with your data observability platform.

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ProjectPro

JANUARY 24, 2023

AWS Glue is a fully managed extract, transform, and load (ETL) service that simplifies the preparation and loading of data for analytics. AWS Glue provides the functionality required by enterprises to build ETL pipelines. The user only needs to define a data pipeline and the processes they want to perform when data flows through it.

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AltexSoft

JUNE 26, 2023

Both data integration and ingestion require building data pipelines — series of automated operations to move data from one system to another. For this task, you need a dedicated specialist — a data engineer or ETL developer. Key differences between structured, semi-structured, and unstructured data.

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Knowledge Hut

SEPTEMBER 26, 2023

Organizations can harness the power of the cloud, easily scaling resources up or down to meet their evolving data processing demands. Supports Structured and Unstructured Data: One of Azure Synapse's standout features is its versatility in handling a wide array of data types.

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Silectis

JANUARY 3, 2021

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 Data engineering is a process by which data engineers make data useful.

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Edureka

FEBRUARY 7, 2023

Azure Data Engineers use a variety of Azure data services, such as Azure Synapse Analytics, Azure Data Factory, Azure Stream Analytics, and Azure Databricks, to design and implement data solutions that meet the needs of their organization. Gain hands-on experience using Azure data services.

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Databand.ai

JULY 19, 2023

Often, the extraction process includes checks and balances to verify the accuracy and completeness of the extracted data. The Load Phase After the data is extracted, it’s loaded into a data storage system in the load phase. The data is loaded as-is, without any transformation.

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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

MAY 28, 2024

Data Transformation : Clean, format, and convert extracted data to ensure consistency and usability for both batch and real-time processing. Data Loading : Load transformed data into the target system, such as a data warehouse or data lake. A typical data ingestion flow.

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Data Ingestion Architecture Designing Hadoop 52

Monte Carlo

JUNE 27, 2023

The Snowflake CEO wasted no time outlining Snowflake’s evolution and expanded ambition, “Most in this room are doing data warehousing. These days we view data warehousing as a particular kind of workload.” He then went on to list Snowflake’s data lake, transactional (Unistore), hybrid, and collaborative (Data Sharing) capabilities.

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Data Lake Government SQL Unstructured Data 59

Knowledge Hut

NOVEMBER 28, 2023

With a plethora of new technology tools on the market, data engineers should update their skill set with continuous learning and data engineer certification programs. What do Data Engineers Do? Let us take a look at the top technical skills that are required by a data engineer first: A. Technical Data Engineer Skills 1.Python

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AltexSoft

DECEMBER 23, 2022

Whether your goal is data analytics or machine learning , success relies on what data pipelines you build and how you do it. But even for experienced data engineers, designing a new data pipeline is a unique journey each time. Data engineering in 14 minutes. The ELT workflow. ELT process steps.

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Knowledge Hut

MAY 23, 2024

With this tool, data science professionals can quickly extract and transform data. It allows integrating various data analysis & data-related components for machine learning (ML) and data mining objective by leveraging its modular data pipelining concept.

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