Snowflake

OCTOBER 16, 2024

Many of our customers — from Marriott to AT&T — start their journey with the Snowflake AI Data Cloud by migrating their data warehousing workloads to the platform. Today we’re focusing on customers who migrated from a legacy data warehouse to Snowflake and some of the benefits they saw.

Digital Media 81

Digital Media Media Data Lake Data Warehouse 81

Striim

SEPTEMBER 11, 2024

Data Collection/Ingestion The next component in the data pipeline is the ingestion layer, which is responsible for collecting and bringing data into the pipeline. By efficiently handling data ingestion, this component sets the stage for effective data processing and analysis.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Monte Carlo

NOVEMBER 14, 2023

Apache Iceberg is an open source data lakehouse table format developed by the data engineering team at Netflix to provide a faster and easier way to process large datasets at scale. It is designed to be easily queryable with SQL even for large analytic tables (we’re talking petabytes of data). Let’s dive in.

Data Lake 64

Data Lake Metadata Data Warehouse SQL 64

Data Engineering Weekly

JULY 7, 2024

Learn More → Notion: Building and scaling Notion’s data lake Notion writes about scaling the data lake by bringing critical data ingestion operations in-house. Hudi seems to be a de facto choice for CDC data lake features.

Data Engineer 98

Data Engineer Data Engineering Engineering Data Lake 98

Rockset

AUGUST 4, 2021

Organizations that depend on data for their success and survival need robust, scalable data architecture, typically employing a data warehouse for analytics needs. Snowflake is often their cloud-native data warehouse of choice. Data ingestion must be performant to handle large amounts of data.

Data Ingestion 40

Data Ingestion Cloud Storage Data Warehouse Architecture 40

ProjectPro

JULY 22, 2016

For the same cost, organizations can now store 50 times as much data as in a Hadoop data lake than in a data warehouse. Data lake is gaining momentum across various organizations and everyone wants to know how to implement a data lake and why.

Data Lake 40

Data Lake Data Warehouse Hadoop Unstructured Data 40

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.

Data Lake 98

Data Lake Data Pipeline Unstructured Data Datasets 98

Data Engineering Podcast

NOVEMBER 20, 2022

Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. RudderStack helps you build a customer data platform on your warehouse or data lake. RudderStack helps you build a customer data platform on your warehouse or data lake.

Data Lake 130

Data Lake Data Ingestion MongoDB Google Cloud 130

Christophe Blefari

JANUARY 20, 2024

Data engineering inherits from years of data practices in US big companies. Hadoop initially led the way with Big Data and distributed computing on-premise to finally land on Modern Data Stack — in the cloud — with a data warehouse at the center. workflows (Airflow, Prefect, Dagster, etc.)

Data Engineer 130

Data Engineer Data Engineering Engineering Hadoop 130

phData: Data Engineering

NOVEMBER 8, 2024

Versioning also ensures a safer experimentation environment, where data scientists can test new models or hypotheses on historical data snapshots without impacting live data. Note : Cloud Data warehouses like Snowflake and Big Query already have a default time travel feature.

Architecture 52

Architecture Systems Data Lake Google Cloud 52

Monte Carlo

NOVEMBER 22, 2024

At the front end, you’ve got your data ingestion layer —the workhorse that pulls in data from everywhere it lives. Gone are the days of just dumping everything into a single database; modern data architectures typically use a combination of data lakes and warehouses.

Data Engineer 52

Data Engineer Data Engineering Building Engineering 52

RandomTrees

SEPTEMBER 27, 2024

A cloud-based software as a service (SaaS) called Microsoft Fabric combines several data and analytics technologies that businesses require. Data Factory, Data Activator, Power BI, Synapse Real-Time Analytics, Synapse Data Engineering, Synapse Data Science, and Synapse Data Warehouse are some of them.

Database-centric 52

Database-centric Pipeline-centric IT BI 52

Monte Carlo

JANUARY 5, 2024

You know what they always say: data lakehouse architecture is like an onion. …ok, Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

You know what they always say: data lakehouse architecture is like an onion. …ok, Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Data Engineering Podcast

OCTOBER 16, 2022

Summary The "data lakehouse" architecture balances the scalability and flexibility of data lakes with the ease of use and transaction support of data warehouses. Mention the podcast to get a free "In Data We Trust World Tour" t-shirt. In fact, while only 3.5%

Data Lake 100

Data Lake Food MongoDB Scala 100

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

DareData

JANUARY 30, 2023

Our goal is to help data scientists better manage their models deployments or work more effectively with their data engineering counterparts, ensuring their models are deployed and maintained in a robust and reliable way. DigDag: An open-source orchestrator for data engineering workflows. Stanford's Relational Databases and SQL.

Data Engineer 52

Data Engineer Data Engineering NoSQL Engineering 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. This feature allows for a more flexible exploration of data.

Data Management 52

Data Management Management Data Lake Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. This feature allows for a more flexible exploration of data.

Data Management 52

Data Management Management Data Lake Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. This feature allows for a more flexible exploration of data.

Data Management 52

Data Management Management Data Lake Data Governance 52

Snowflake

SEPTEMBER 14, 2023

Unbound by the limitations of a legacy on-premises solution, its multi-cluster shared data architecture separates compute from storage, allowing data teams to easily scale up and down based on their needs. Data engineers spent 20% of their time on infrastructure issues such as tuning Spark jobs.

Data Architecture 99

Data Architecture Architecture Hadoop Data Warehouse 99

Data Engineering Podcast

NOVEMBER 6, 2022

RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control.

MongoDB 130

MongoDB Scala MySQL Machine Learning 130

Knowledge Hut

MARCH 28, 2024

Role Level Intermediate Responsibilities Design and develop data pipelines to ingest, process, and transform data. Implemented and managed data storage solutions using Azure services like Azure SQL Database , Azure Data Lake Storage, and Azure Cosmos DB.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Warehouse 52

Data Engineering Podcast

OCTOBER 30, 2022

Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. RudderStack helps you build a customer data platform on your warehouse or data lake. RudderStack helps you build a customer data platform on your warehouse or data lake.

Engineering 100

Engineering MongoDB Scala MySQL 100

Christophe Blefari

MARCH 4, 2023

When you activate the query acceleration service when Snowflake thinks that a query can be accelerated it will launch more compute than actually specified by your warehouse. This is funny to see their offering because they offer a "managed data warehouse storage", which means without the compute. At least for me.

Machine Learning 130

Machine Learning AWS Data Data Lake 130

Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? The modern data stack era , roughly 2017 to present data, saw the widespread adoption of cloud computing and modern data repositories that decoupled storage from compute such as data warehouses, data lakes, and data lakehouses.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Cloudera

JUNE 30, 2022

Over the past decade, Cloudera has enabled multi-function analytics on data lakes through the introduction of the Hive table format and Hive ACID. Companies, on the other hand, have continued to demand highly scalable and flexible analytic engines and services on the data lake, without vendor lock-in.

Data Lake 86

Data Lake Business Intelligence Metadata Data Warehouse 86

Edureka

FEBRUARY 9, 2023

As the demand for data engineers grows, having a well-written resume that stands out from the crowd is critical. Azure data engineers are essential in the design, implementation, and upkeep of cloud-based data solutions. It is also crucial to have experience with data ingestion and transformation.

Data Engineer 52

Data Engineer Data Engineering Engineering Amazon Web Services 52

Striim

OCTOBER 11, 2024

Then, we’ll explore a data pipeline example and dive deeper into the key differences between a traditional data pipeline vs ETL. What is a Data Pipeline? A data pipeline refers to a series of processes that transport data from one or more sources to a destination, such as a data warehouse, database, or application.

Data Pipeline 52

Data Pipeline MongoDB Unstructured Data Data Lake 52

Data Engineering Weekly

MARCH 24, 2024

Dive into Spyne's experience with: - Their search for query acceleration with pre-aggregations and caching - Developing new functionality with Open AI - Optimizing query cost with their data warehouse [link] Suresh Hasuni: Cost Optimization Strategies for Scalable Data Lakehouse Cost is the major concern as the adoption of data lakes increases.

Data Engineer 62

Data Engineer Data Engineering Engineering Metadata 62

Data Engineering Podcast

NOVEMBER 13, 2022

Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. RudderStack helps you build a customer data platform on your warehouse or data lake. RudderStack helps you build a customer data platform on your warehouse or data lake.

MongoDB 100

MongoDB Google Cloud Scala MySQL 100

Data Engineering Podcast

SEPTEMBER 25, 2022

Mention the podcast to get a free "In Data We Trust World Tour" t-shirt. RudderStack helps you build a customer data platform on your warehouse or data lake. report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months.

Food 100

Food MongoDB Scala MySQL 100

Knowledge Hut

NOVEMBER 2, 2023

Top 10 Azure Data Engineering Project Ideas for Beginners For beginners looking to gain practical experience in Azure Data Engineering, here are 10 Azure Data engineer real time projects ideas that cover various aspects of data processing, storage, analysis, and visualization using Azure services: 1.

Data Engineer 52

Data Engineer Data Engineering Project Coding 52

Knowledge Hut

SEPTEMBER 26, 2023

Microsoft Azure's Azure Synapse, formerly known as Azure SQL Data Warehouse, is a complete analytics offering. Designed to tackle the challenges of modern data management and analytics, Azure Synapse brings together the worlds of big data and data warehousing into a unified and seamlessly integrated platform.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

Towards Data Science

JULY 24, 2023

A data engineering manager at a Fortune 500 company expressed the pain of on-prem limitations to me by saying: “Our analysts were unable to run the queries they wanted to run when they wanted to run them. Why are these things related, and more importantly, why should data leaders care? Double check any requirements that say otherwise.

Metadata 95

Metadata Data Warehouse Data Lake Data Science 95

Data Engineering Podcast

JULY 3, 2022

In order to quickly identify if and how two data systems are out of sync Gleb Mezhanskiy and Simon Eskildsen partnered to create the open source data-diff utility. report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. In fact, while only 3.5%

Data Integration 100

Data Integration MongoDB Scala MySQL 100

Cloudera

AUGUST 9, 2022

Data platforms are no longer skunkworks projects or science experiments. As customers import their mainframe and legacy data warehouse workloads, there is an expectation on the platform that it can meet, if not exceed, the resilience of the prior system and its associated dependencies. Conclusion.

Data Lake 77

Data Lake Data Warehouse Architecture Data Ingestion 77