Hepta Analytics

FEBRUARY 14, 2022

DE Zoomcamp 2.2.1 – Introduction to Workflow Orchestration Following last weeks blog , we move to data ingestion. We already had a script that downloaded a csv file, processed the data and pushed the data to postgres database. This week, we got to think about our data ingestion design.

Data Ingestion 52

Data Ingestion Data Engineering Data Engineer Engineering 52

Sync Computing

NOVEMBER 7, 2024

Data volume and velocity, governance, structure, and regulatory requirements have all evolved and continue to. Despite these limitations, data warehouses, introduced in the late 1980s based on ideas developed even earlier, remain in widespread use today for certain business intelligence and data analysis applications.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Unstructured Data 52

Monte Carlo

MARCH 28, 2023

As part of this movement, Fivetran and dbt fundamentally altered the data pipeline from ETL to ELT. Hightouch interrupted SaaS eating the world in an attempt to shift the center of gravity to the data warehouse. Other common light transformations done within the ingestion phase are data formatting and deduplication.

Data Warehouse 126

Data Warehouse Raw Data Data Pipeline Software Engineer 126

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 Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

Cloudera

FEBRUARY 8, 2021

ECC will enrich the data collected and will make it available to be used in analysis and model creation later in the data lifecycle. Below is the entire set of steps in the data lifecycle, and each step in the lifecycle will be supported by a dedicated blog post(see Fig. 2 ECC data enrichment pipeline.

Data Pipeline 118

Data Pipeline Building Manufacturing Data Warehouse 118

Data Engineering Podcast

JUNE 5, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking all of that information into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Go to dataengineeringpodcast.com/atlan today to learn more about how you can take advantage of active metadata and escape the chaos.

Data Security 100

Data Security Metadata MongoDB MySQL 100

Cloudera

AUGUST 31, 2021

Cloudera and Accenture demonstrate strength in their relationship with an accelerator called the Smart Data Transition Toolkit for migration of legacy data warehouses into Cloudera Data Platform. Accenture’s Smart Data Transition Toolkit . Are you looking for your data warehouse to support the hybrid multi-cloud?

Data Warehouse 82

Data Warehouse Database-centric Metadata Cloud 82

Data Engineering Weekly

JULY 7, 2024

Experience Enterprise-Grade Apache Airflow Astro augments Airflow with enterprise-grade features to enhance productivity, meet scalability and availability demands across your data pipelines, and more. Hudi seems to be a de facto choice for CDC data lake features. Notion migrated the insert heavy workload from Snowflake to Hudi.

Data Engineering 98

Data Engineering Data Engineer Engineering Data Lake 98

Netflix Tech

MARCH 25, 2019

Therefore, the ingestion approach for data lineage is designed to work with many disparate data sources. Our data ingestion approach, in a nutshell, is classified broadly into two buckets?—?push We leverage Metacat data, our internal metadata store and service, to enrich lineage data with additional table metadata.

Building 90

Building Metadata Transportation Data Ingestion 90

Data Engineering Weekly

MAY 16, 2023

WAP [Write-Audit-Publish] Pattern The WAP pattern follows a three-step process Write Phase The write phase results from a data ingestion or data transformation step. In the 'Write' stage, we capture the computed data in a log or a staging area. Event Routers can add additional metadata to the envelope of the event.

Engineering 110

Engineering Kafka Data Pipeline Data Warehouse 110

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 98

Metadata Data Warehouse Data Lake Data Science 98

Towards Data Science

NOVEMBER 4, 2023

Often it is a data warehouse solution (DWH) in the central part of our infrastructure. Data warehouse exmaple. Indeed, why would we build a data connector from scratch if it already exists and is being managed in the cloud? The downside of this approach is it’s pricing model though.

Data Engineering 98

Data Engineering Data Engineer Engineering BI 98

Cloudera

DECEMBER 15, 2022

Since we announced the general availability of Apache Iceberg in Cloudera Data Platform (CDP), Cloudera customers, such as Teranet , have built open lakehouses to future-proof their data platforms for all their analytical workloads. Only metadata will be regenerated. Data quality using table rollback.

Cloud 80

Cloud Metadata Data Warehouse Google Cloud 80

Cloudera

SEPTEMBER 9, 2022

analyst Sumit Pal, in “Exploring Lakehouse Architecture and Use Cases,” published January 11, 2022: “Data lakehouses integrate and unify the capabilities of data warehouses and data lakes, aiming to support AI, BI, ML, and data engineering on a single platform.” Iceberg handles massive data born in the cloud.

Architecture 102

Architecture Metadata Machine Learning Unstructured Data 102

Cloudera

JUNE 30, 2022

With Cloudera’s vision of hybrid data , enterprises adopting an open data lakehouse can easily get application interoperability and portability to and from on premises environments and any public cloud without worrying about data scaling. Why integrate Apache Iceberg with Cloudera Data Platform?

Data Lake 88

Data Lake Business Intelligence Metadata Data Warehouse 88

Towards Data Science

DECEMBER 1, 2023

This tool automates ELT (Extract, Load, Transform) process, integrating your data from the source system of Google Calendar to our Snowflake data warehouse. Storage —  Snowflake Snowflake, a cloud-based data warehouse tailored for analytical needs, will serve as our data storage solution.

Data Engineering 97

Data Engineering Data Engineer Project Engineering 97

Towards Data Science

APRIL 3, 2023

As part of this movement, Fivetran and dbt fundamentally altered the data pipeline from ETL to ELT. Hightouch interrupted SaaS eating the world in an attempt to shift the center of gravity to the data warehouse. Other common light transformations done within the ingestion phase are data formatting and deduplication.

Data Engineering 98

Data Engineering Data Engineer Engineering Data Warehouse 98

Data Engineering Podcast

AUGUST 6, 2022

Summary The optimal format for storage and retrieval of data is dependent on how it is going to be used. For analytical systems there are decades of investment in data warehouses and various modeling techniques. What are some of the data management considerations that are introduced by vector databases?

Machine Learning 100

Machine Learning Database MySQL PostgreSQL 100

ProjectPro

FEBRUARY 8, 2023

It offers users a data integration tool that organizes data from many sources, formats it, and stores it in a single repository, such as data lakes, data warehouses, etc., Glue uses ETL jobs for extracting data from various AWS cloud services and integrating it into data warehouses and lakes.

AWS 98

AWS Scala Metadata Data Lake 98

RandomTrees

SEPTEMBER 6, 2020

Snowflake Overview A data warehouse is a critical part of any business organization. Lot of cloud-based data warehouses are available in the market today, out of which let us focus on Snowflake. Snowflake is an analytical data warehouse that is provided as Software-as-a-Service (SaaS).

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 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 Engineering 62

Data Engineering Data Engineer Engineering Metadata 62

Databand.ai

JUNE 28, 2023

This might include processes like data extraction from different sources, data cleansing, data transformation (like aggregation), and loading the data into a database or a data warehouse. Data storage and delivery : Observability continues into the storage and delivery phase.

Data Pipeline 98

Data Pipeline Data Engineering Data Engineer Engineering 98

Cloudera

JUNE 16, 2022

Faster data ingestion: streaming ingestion pipelines. The DevOps/app dev team wants to know how data flows between such entities and understand the key performance metrics (KPMs) of these entities. She is a smart data analyst and former DBA working at a planet-scale manufacturing company.

Kafka 88

Kafka Manufacturing Data Lake SQL 88

phData: Data Engineering

SEPTEMBER 19, 2023

Want to learn more about data governance? Check out our Data Governance on Snowflake blog! Metadata Management Data modeling methodologies help in managing metadata within the data lake. Metadata describes the characteristics, attributes, and context of the data.

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Monte Carlo

APRIL 4, 2023

That’s why, in addition to integrating with your central data warehouse , lake , and lakehouse , Monte Carlo also integrates with transformation , orchestration , and now data ingestion tools. A modified dbt model? Failed Airflow job? None of the above?

BI 64

BI Data Ingestion Data Pipeline Metadata 64

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. Ingestion layer 2.

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. Ingestion layer 2.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

NOVEMBER 14, 2023

Databricks announced that Delta tables metadata will also be compatible with the Iceberg format, and Snowflake has also been moving aggressively to integrate with Iceberg. It is designed to be easily queryable with SQL even for large analytic tables (we’re talking petabytes of data). How Apache Iceberg tables structure metadata.

Data Lake 64

Data Lake Metadata Data Warehouse SQL 64

Cloudyard

MARCH 4, 2023

DCDW Architecture Above all, Architecture was divided into three Business layers: Firstly,Agile Data ingestion : Heterogeneous Source System fed the data into Cloud. Respective Cloud would consume/Store the data in bucket or containers. Load the data AS-IS into Snowflake called RAW layer.

Architecture 52

Architecture Cloud Metadata Data Ingestion 52

Towards Data Science

MARCH 5, 2024

At its core, BigQuery is a serverless Data Warehouse for analytical purposes and built-in features like Machine Learning ( BigQuery ML ). Traditionally, normalization has been hailed as a best practice, emphasizing the reduction of redundancy and the preservation of data integrity. Also this query comes at 0 costs.

Bytes 97

Bytes Google Cloud Cloud Storage Utilities 97

Cloudera

FEBRUARY 16, 2022

Weak model lineage can result in reduced model performance, a lack of confidence in model predictions and potentially violation of company, industry or legal regulations on how data is used. . Within the CML data service, model lineage is managed and tracked at a project level by the SDX. Figure 03: lineage.yaml.

Machine Learning 85

Machine Learning Algorithm Government Metadata 85

Precisely

MAY 13, 2024

Wide support for enterprise-grade sources and targets Large organizations with complex IT landscapes must have the capability to easily connect to a wide variety of data sources. Whether it’s a cloud data warehouse or a mainframe, look for vendors who have a wide range of capabilities that can adapt to your changing needs.

Data Integration 52

Data Integration Metadata Amazon Web Services Data Governance 52

AltexSoft

AUGUST 29, 2023

Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture. This structure is made efficient by data engineering practices that include object storage. Data warehouse vs. data lake in a nutshell.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Netflix Tech

DECEMBER 14, 2018

A fundamental requirement for any lasting data system is that it should scale along with the growth of the business applications it wishes to serve. NMDB is built to be a highly scalable, multi-tenant, media metadata system that can serve a high volume of write/read throughput as well as support near real-time queries.

Media 97

Media Database Metadata Data Schemas 97

Data Engineering Weekly

OCTOBER 30, 2022

DuckDB is gaining much attention on this promise, and the Dagster team writes about its experimental data warehouse built on top of DuckDB, Parquet, and Dagster. link] Sponsored: Why You Should Care About Dimensional Data Modeling It's easy to overlook all of the magic that happens inside the data warehouse.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Ingestion 52

Monte Carlo

APRIL 24, 2023

Traditionally, after being stored in a data lake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption. Databricks Data Catalog and AWS Lake Formation are examples in this vein. See our post: Data Lakes vs. Data Warehouses.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Databand.ai

AUGUST 30, 2023

DataOps , short for data operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data processes across an organization. These tools help organizations implement DataOps practices by providing a unified platform for data teams to collaborate, share, and manage their data assets.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52