U-Next

SEPTEMBER 7, 2022

The terms “ Data Warehouse ” and “ Data Lake ” may have confused you, and you have some questions. Structuring data refers to converting unstructured data into tables and defining data types and relationships based on a schema. What is Data Lake? . Athena on AWS. .

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 52

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? Why is data pipeline architecture important?

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

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?

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Monte Carlo

APRIL 24, 2023

Data ingestion When we think about the flow of data in a pipeline, data ingestion is where the data first enters our platform. There are two primary types of raw data. But in many growing organizations, a combination of Fivetran and custom pipelines using Airflow will usually do the trick.

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

Grouparoo

OCTOBER 26, 2021

As a result, data has to be moved between the source and destination systems and this is usually done with the aid of data pipelines. What is a Data Pipeline? A data pipeline is a set of processes that enable the movement and transformation of data from different sources to destinations.

Data Pipeline 52

Data Pipeline ETL Tools Data Warehouse ETL System 52

Monte Carlo

NOVEMBER 14, 2023

In this piece, we break down popular Iceberg use cases, advantages and disadvantages, and its impact on data quality so you can make the table format decision that’s right for your team. Is your data lake a good fit for Iceberg? Let’s dive in.

Data Lake 64

Data Lake Metadata Data Warehouse SQL 64

DataKitchen

JULY 27, 2023

The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure. While working in Azure with our customers, we have noticed several standard Azure tools people use to develop data pipelines and ETL or ELT processes. We counted ten ‘standard’ ways to transform and set up batch data pipelines in Microsoft Azure.

Data Pipeline 52

Data Pipeline BI Machine Learning Data Preparation 52

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.

Data Lake 52

Data Lake Unstructured Data Data Warehouse Metadata 52

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.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

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?

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

DareData

JULY 29, 2020

With these points in mind, I argue that the biggest hurdle to the widespread adoption of these advanced techniques in the healthcare industry is not intrinsic to the industry itself, or in any way related to its practitioners or patients, but simply the current lack of high-quality data pipelines. What makes a good Data Pipeline?

Data Pipeline 52

Data Pipeline Healthcare Medical Pipeline-centric 52

Ascend.io

APRIL 12, 2023

From this research, we developed a framework with a sequence of stages to implement data integrity quickly and measurably via data pipelines. Table of Contents Why does data integrity matter? At every level of a business, individuals must trust the data, so they can confidently make timely decisions. Let’s explore!

Data Pipeline 52

Data Pipeline Data Integration Datasets Data 52

Towards Data Science

FEBRUARY 6, 2024

As you do not want to start your development with uncertainty, you decide to go for the operational raw data directly. Accessing Operational Data I used to connect to views in transactional databases or APIs offered by operational systems to request the raw data. Does it sound familiar?

Systems 78

Systems Raw Data Metadata Data Cleanse 78

Ascend.io

SEPTEMBER 16, 2024

How to Design a Data Flow in 6 Steps: 1. Identify Data Sources 3. Define Data Requirements 4. Implement Data Flow Diagrams (DFDs) 5. Build & Optimize Data Pipelines 6. But what are data flows? In this post, we’ll explore how to design, build, and scale your data flows.

Designing 52

Designing Cloud Computing Data Pipeline Raw Data 52

WeCloudData

OCTOBER 19, 2021

The client needed to build its own internal data pipeline with enough flexibility to meet the business requirements for a job market analysis platform & dashboard. The client intends to build on and improve this data pipeline by moving towards a more serverless architecture and adding DevOps tools & workflows.

Consulting 52

Consulting Raw Data Data Lake Data Pipeline 52

WeCloudData

OCTOBER 19, 2021

The client needed to build its own internal data pipeline with enough flexibility to meet the business requirements for a job market analysis platform & dashboard. The client intends to build on and improve this data pipeline by moving towards a more serverless architecture and adding DevOps tools & workflows.

Consulting 52

Consulting Raw Data Data Lake Data Pipeline 52

ThoughtSpot

SEPTEMBER 6, 2023

Those coveted insights live at the end of a process lovingly known as the data pipeline. The pathway from ETL to actionable analytics can often feel disconnected and cumbersome, leading to frustration for data teams and long wait times for business users. uptime and full data security compliance. And that’s all with 99.9%

Data Workflow 105

Data Workflow Raw Data Data Lake Business Intelligence 105

Towards Data Science

MAY 3, 2023

One such tool is the Versatile Data Kit (VDK), which offers a comprehensive solution for controlling your data versioning needs. VDK helps you easily perform complex operations, such as data ingestion and processing from different sources, using SQL or Python. Use VDK to build a data lake and merge multiple sources.

Data Lake 82

Data Lake SQL Data Data Warehouse 82

Data Engineering Podcast

DECEMBER 11, 2021

In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Start trusting your data with Monte Carlo today! (e.g. constraints on data manipulation, security, privacy concerns, etc.)

Data Warehouse 100

Data Warehouse Raw Data Data Lake BI 100

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.

Building 78

Building Transportation Data Lake Metadata 78

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.

Data Cleanse 52

Data Cleanse Raw Data Data Storage Data Warehouse 52

DataKitchen

AUGUST 9, 2021

The data industry has a wide variety of approaches and philosophies for managing data: Inman data factory, Kimball methodology, s tar schema , or the data vault pattern, which can be a great way to store and organize raw data, and more. Data mesh does not replace or require any of these.

Pharmaceutical 98

Pharmaceutical Raw Data Data Lake Data 98

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.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

Ascend.io

NOVEMBER 21, 2023

More importantly, we will contextualize ELT in the current scenario, where data is perpetually in motion, and the boundaries of innovation are constantly being redrawn. Extract The initial stage of the ELT process is the extraction of data from various source systems. What Is ELT? So, what exactly is ELT?

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

Data Engineering Podcast

JUNE 26, 2022

Summary The most complicated part of data engineering is the effort involved in making the raw data fit into the narrative of the business. Shorten development cycles, eliminate the need for cumbersome data pipeline work, and mathematically guarantee the privacy of your data, with Tonic.ai.

Data Management 100

Data Management Management MongoDB Scala 100

Ascend.io

DECEMBER 11, 2023

Here’s the deal: for data to truly drive your business forward, you need a reliable and scalable system to keep it moving without hiccups. In other words, you need data orchestration. In this article, we’ll break down what data orchestration is, its significance, and how it differs from data pipeline orchestration.

Data Workflow 52

Data Workflow Data Pipeline Data Lake Data 52

Data Engineering Podcast

JANUARY 14, 2017

Summary Do you wish that you could track the changes in your data the same way that you track the changes in your code? Pachyderm is a platform for building a data lake with a versioned file system. Interview with Daniel Whitenack Introduction How did you get started in the data engineering space?

Data Lake 100

Data Lake Raw Data Kafka Data Engineer 100

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.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

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

Data Lake 52

Data Lake Metadata AWS Data Warehouse 52

Databand.ai

AUGUST 30, 2023

Data storage platforms can include traditional relational databases, NoSQL databases, data lakes, or cloud-based storage services. A DataOps architecture must consider the performance, scalability, and cost implications of the chosen data storage platform.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

DataKitchen

NOVEMBER 29, 2021

The rise of distributed data architectures like Data Mesh will combine with DataOps automation to give rise to Hub-Spoke architectures that deftly blend the benefits of centralization and decentralization. For example, a Hub-Spoke architecture could integrate data from a multitude of sources into a data lake.

Data Lake 98

Data Lake Manufacturing Architecture Data Architecture 98

Ascend.io

MAY 18, 2023

In this article, we’ll: Examine the evolution of the data stack Discuss the issues that have arisen from the modern data stack complexity Explore the next steps in the innovation cycle for data engineering The Evolution of the Data Stack Before we dive into the backstory of how we got here, let’s define what a data stack is.

Data Warehouse 52

Data Warehouse Data Pipeline Data Lake Data Engineering 52

Monte Carlo

MARCH 14, 2023

Data from these sources are often ingested into a cloud-based data warehouse or data lake , where they can then be mined for information and insights. Source : Fundamentals of Data Engineering by Joe Reis and Matt Housley. Some data teams will leverage micro-batch strategies for time sensitive use cases.

Data Ingestion 52

Data Ingestion Data Warehouse Lambda Architecture Raw Data 52

Monte Carlo

MAY 30, 2023

It’s designed to improve upon the performance and usability challenges of older data storage formats such as Apache Hive and Apache Parquet. Schema changes can still create challenges downstream in the data pipeline and may require data observability or other data monitoring to fully prevent data quality issues.

Metadata 52

Metadata Raw Data Data Lake Data 52

ProjectPro

FEBRUARY 16, 2023

ETL Architecture on AWS: Examining the Scalable Architecture for Data Transformation ETL Architecture on AWS typically consists of three components - Source Data Store A Data Transformation Layer Target Data Store Source Data Store The source data store is where raw data is stored before being transformed and loaded into the target data store.

AWS 52

AWS Data Management ETL Tools Management 52

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. Scalability.

Process 52

Process Building Raw Data Data Lake 52

Knowledge Hut

DECEMBER 28, 2023

The Azure Data Engineer certification imparts to them a deep understanding of data processing, storage and architecture. By leveraging their proficiency, they enable organizations to transform raw data into valuable insights that drive business decisions. What is the Azure Data Engineer Certification?

Certification 52

Certification Data Engineer Data Engineering Engineering 52