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

Data Engineering Podcast

SEPTEMBER 4, 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. RudderStack helps you build a customer data platform on your warehouse or data lake.

IT 100

IT Metadata MongoDB MySQL 100

Data Engineering Podcast

DECEMBER 19, 2021

Summary Building a well managed data ecosystem for your organization requires a holistic view of all of the producers, consumers, and processors of information. The team at Metaphor are building a fully connected metadata layer to provide both technical and social intelligence about your data.

Systems 100

Systems Building Metadata Data Warehouse 100

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

Data Engineering Podcast

NOVEMBER 20, 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! Start trusting your data with Monte Carlo today!

Data Warehouse 100

Data Warehouse Data Lake BI Business Intelligence 100

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. Big Data Large volumes of structured or unstructured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

DataKitchen

AUGUST 3, 2021

First-generation – expensive, proprietary enterprise data warehouse and business intelligence platforms maintained by a specialized team drowning in technical debt. Second-generation – gigantic, complex data lake maintained by a specialized team drowning in technical debt. The product includes data and operations.

Pharmaceutical 98

Pharmaceutical Data Lake Data Architecture Scala 98

Monte Carlo

MARCH 3, 2021

After all, any solution dedicated to keeping data pipelines reliable should take its own medicine when it comes to building the actual product. Instead, we only extract query logs, metadata, and aggregated statistics about data usage to ensure that your most critical data assets are as trustworthy and reliable as possible.

Metadata 52

Metadata Data Lake Business Intelligence Certification 52

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.);

Data Architect 87

Data Architect Certification Generalist Big Data 87

Data Engineering Podcast

DECEMBER 29, 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. Struggling with broken pipelines? Missing data? Atlan is the metadata hub for your data ecosystem.

Management 130

Management Metadata Business Intelligence Data Lake 130

Monte Carlo

AUGUST 25, 2023

That’s why it’s essential for teams to choose the right architecture for the storage layer of their data stack. But, the options for data storage are evolving quickly. Different vendors offering data warehouses, data lakes, and now data lakehouses all offer their own distinct advantages and disadvantages for data teams to consider.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 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? What is data pipeline architecture?

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Data Engineering Podcast

DECEMBER 18, 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. Struggling with broken pipelines? Missing data? Atlan is the metadata hub for your data ecosystem.

Metadata 130

Metadata Business Intelligence Data Lake BI 130

Data Engineering Podcast

DECEMBER 25, 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. Struggling with broken pipelines? Missing data? Again, be prepared to have metadata challenges especially.

Building 100

Building Metadata Business Intelligence Data Lake 100

Data Engineering Podcast

NOVEMBER 27, 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. Struggling with broken pipelines? Missing data? Stale dashboards? If this resonates with you, you’re not alone.

Data Process 100

Data Process Process Metadata Business Intelligence 100

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

Data Lake 52

Data Lake Metadata Bytes Google Cloud 52

Cloudera

AUGUST 30, 2022

Data Lakehouse: Data lakehouses integrate and unify the capabilities of data warehouses and data lakes, aiming to support artificial intelligence, business intelligence, machine learning, and data engineering use cases on a single platform. Towards Data Science ). Forrester ).

Data Architecture 76

Data Architecture Architecture Data Lake NoSQL 76

AltexSoft

OCTOBER 30, 2021

They’re integral specialists in data science projects and cooperate with data scientists by backing up their algorithms with solid data pipelines. Juxtaposing data scientist vs engineer tasks. One data scientist usually needs two or three data engineers. Managing data and metadata.

Data Engineering 98

Data Engineering Data Engineer Engineering Machine Learning 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

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 vs ETL. Enrichment.

Process 52

Process Building Raw Data Data Lake 52

Monte Carlo

DECEMBER 14, 2022

We’ve long supported business intelligence tools like Looker, Tableau, and Mode, so it was only a matter of time until we supported Microsoft’s PowerBI. In other words, you can now trace how data flows up and downstream across your data lake similar to how we map table dependencies in data warehouse environments.

Data Lake 52

Data Lake BI Business Intelligence Metadata 52

DataKitchen

OCTOBER 11, 2021

The biggest challenge is broken data pipelines due to highly manual processes. Figure 1 shows a manually executed data analytics pipeline. The data engineer then emails the BI Team, who refreshes a Tableau dashboard. Figure 1: Example data pipeline with manual processes. Monitoring Job Metadata.

Engineering 60

Engineering BI Business Analyst Metadata 60

Monte Carlo

SEPTEMBER 23, 2021

Data observability , an organization’s ability to fully understand the health of the data in their ecosystem, eliminates data downtime by applying best practices of DevOps and application observability to data pipelines. Throughout this time, data is transformed, often more than once.

BI 52

BI Data Metadata Data Pipeline 52

Monte Carlo

MAY 13, 2021

Here’s how to do that with Snowflake: This query will fetch a list of all tables along with helpful metadata about their settings. The comment property is particularly useful if you’ve been diligent about documenting your data with COMMENT. Luckily, Snowflake tracks that information as writes are made to tables in the warehouse.

SQL 40

SQL Metadata Bytes Data Pipeline 40

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

AltexSoft

JUNE 26, 2023

Data collection is a methodical practice aimed at acquiring meaningful information to build a consistent and complete dataset for a specific business purpose — such as decision-making, answering research questions, or strategic planning. For this task, you need a dedicated specialist — a data engineer or ETL developer.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

AltexSoft

JULY 29, 2022

According to the study by the Business Application Research Center (BARC), Hadoop found intensive use as. a suitable technology to implement data lake architecture. Hadoop 3 expanded this list with Microsoft Azure Data Lake and Aliyun Object Storage System. If you divide it by 128 MB, you’ll have 8 data blocks.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Advancing Analytics: Data Engineering

NOVEMBER 10, 2020

We have discussed in the past this idea of the lakehouse , the aspirational target of many analytics platforms these days of combining the huge power and potential of data lakes with the rigour, reliability and concurrency of a data warehouse. The Modern Data Warehouse. The Lakehouse Architecture.

SQL 52

SQL BI Data Warehouse Data Lake 52

ProjectPro

JANUARY 31, 2022

This is the reason why we need Data Warehouses. What is Snowflake Data Warehouse? A Data Warehouse is a central information repository that enables Data Analytics and Business Intelligence (BI) activities. This layer stores the metadata needed to optimize a query or filter data.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

ProjectPro

NOVEMBER 30, 2021

This guide provides definitions, a step-by-step tutorial, and a few best practices to help you understand ETL pipelines and how they differ from data pipelines. The crux of all data-driven solutions or business decision-making lies in how well the respective businesses collect, transform, and store data.

Process 52

Process Data Pipeline Data Warehouse AWS 52

ProjectPro

AUGUST 24, 2021

Data Sourcing: Building pipelines to source data from different company data warehouses is fundamental to the responsibilities of a data engineer. So, work on projects that guide you on how to build end-to-end ETL/ELT data pipelines. Upload it to Azure Data lake storage manually.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

AltexSoft

OCTOBER 8, 2021

They are applied to retrieve data from the source systems, perform transformations when necessary, and load it into a target system ( data mart , data warehouse, or data lake). So, why is data integration such a big deal? Connections to both data warehouses and data lakes are possible in any case.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

ProjectPro

JANUARY 31, 2023

Why is HDFS only suitable for large data sets and not the correct tool for many small files? NameNode is often given a large space to contain metadata for large-scale files. The metadata should come from a single file for optimal space use and economic benefit. And storing these metadata in RAM will become problematic.

Big Data 52

Big Data Hadoop AWS Relational Database 52

ProjectPro

OCTOBER 20, 2021

Having multiple data integration routes helps optimize the operational as well as analytical use of data. Experimentation in production Big Data Data Warehouse for core ETL tasks Direct data pipelines Tiered Data Lake 4. 29) What is the difference between MLOps and DevOps?

Machine Learning 52

Machine Learning Algorithm Government Data Science 52

Cloudera

JUNE 16, 2022

CSP was recently recognized as a leader in the 2022 GigaOm Radar for Streaming Data Platforms report. The DevOps/app dev team wants to know how data flows between such entities and understand the key performance metrics (KPMs) of these entities. Without context, streaming data is useless.”

Kafka 84

Kafka Manufacturing Data Lake SQL 84

Monte Carlo

JANUARY 23, 2024

The speed of ETL and analytics has increased Maxime recalls a time, not too long ago, when data engineering would require running Hive jobs for hours at a time, frequent context switching between jobs and managing different elements of your data pipeline. Data debt or data pipeline chaos.

Data Engineering 98

Data Engineering Data Engineer Engineering Data 98

Monte Carlo

JANUARY 12, 2024

On the data lake side, Databricks has launched Unity Catalog to help bring more metadata, structure, and governance to data assets. Real-time data/insights Being able to access real-time data for analysis might sound like overkill to some, but that’s just no longer the case.

Big Data 59

Big Data Data Analytics Data Science Data Lake 59