Cloudyard

APRIL 4, 2024

Implementation: Implementation: We developed a Snowpark script to process customer transaction data stored in the Snowflake database. Data Ingestion: Reads customer transaction data from the “CUSTOMER_TRANSACTIONS” table in the Snowflake database. The script performs the following tasks: 1.Data

Retail 40

Retail Data Ingestion Metadata Datasets 40

phData: Data Engineering

SEPTEMBER 19, 2023

Data Governance and Security By defining data models, organizations can establish policies, access controls, and security measures to protect sensitive data. Data models can also facilitate compliance with regulations and ensure proper data handling and protection. Want to learn more about data governance?

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Towards Data Science

DECEMBER 1, 2023

The data volume we will deal with is small, so we will not try to overkill with data partitioning, time travel, Snowpark, and other Snowflake advanced capabilities. However, we will pay particular attention to Access Control (this will be used for dbt access). There are two ways to access dbt: dbt Cloud and dbt Core.

Data Engineering 73

Data Engineering Data Engineer Project Engineering 73

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. Watch our video explaining how data engineering works.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Rockset

APRIL 18, 2023

Vector search has seen an explosion in popularity due to improvements in accuracy and broadened accessibility to the models used to generate embeddings. Rockset offers a number of benefits along with vector search support to create relevant experiences: Real-Time Data: Ingest and index incoming data in real-time with support for updates.

Unstructured Data 105

Unstructured Data Metadata Machine Learning SQL 105

Data Engineering Weekly

OCTOBER 30, 2022

Before you add another tool to your data stack, check out our latest guide for 10 things to consider when evaluating data observability platforms, including scalability, time to value, and ease of setup. The modern data stack is trying to address this problem in a silo; the org eventually has to tie everything to make it work.

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. AWS is one of the most popular data lake vendors.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

RandomTrees

SEPTEMBER 6, 2020

The architecture is three layered: Database Storage: Snowflake has a mechanism to reorganize the data into its internal optimized, compressed and columnar format and stores this optimized data in cloud storage. This stage handles all the aspects of data storage like organization, file size, structure, compression, metadata, statistics.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

Cloudera

FEBRUARY 9, 2021

Today’s customers have a growing need for a faster end to end data ingestion to meet the expected speed of insights and overall business demand. This ‘need for speed’ drives a rethink on building a more modern data warehouse solution, one that balances speed with platform cost management, performance, and reliability.

Data Warehouse 67

Data Warehouse Cloud Kafka Cloud Storage 67

Cloudera

SEPTEMBER 28, 2020

Attribute-based access control and SparkSQL fine-grained access control. Lineage and chain of custody, advanced data discovery and business glossary. Store and access schemas across clusters and rebalance clusters with Cruise Control. Data Science and machine learning workloads using CDSW. Ranger 2.0. on roadmap).

Cloud 130

Cloud Kafka Professional Services Metadata 130

Cloudera

FEBRUARY 16, 2022

Within the CML data service, model lineage is managed and tracked at a project level by the SDX. SDX provides open metadata management and governance across each deployed environment by allowing organisations to catalogue, classify as well as control access to and manage all data assets. Figure 03: lineage.yaml.

Machine Learning 82

Machine Learning Algorithm Government Metadata 82

Cloudera

AUGUST 31, 2021

Running on CDW is fully integrated with streaming, data engineering, and machine learning analytics. It has a consistent framework that secures and provides governance for all data and metadata on private clouds, multiple public clouds, or hybrid clouds. Consideration of both data & metadata in the migration.

Data Warehouse 82

Data Warehouse Database-centric Metadata Cloud 82

Databand.ai

JUNE 28, 2023

Having a bigger and more specialized data team can help, but it can hurt if those team members don’t coordinate. More people accessing the data and running their own pipelines and their own transformations causes errors and impacts data stability. is a unified data observability platform built for data engineers.

Data Pipeline 98

Data Pipeline Data Engineering Data Engineer Engineering 98

dbt Developer Hub

JANUARY 14, 2023

The builder: I’m a data freelancer who deploys end to end solutions, so when I have a data problem, I cannot just let it go. dlt is a new Python library for declarative data ingestion which I have wanted to test for some time. The dlt pipeline I created for ingestion is in this repo.

Data Ingestion 52

Data Ingestion Google Cloud Python Cloud 52

ProjectPro

FEBRUARY 25, 2022

Data Ingestion Data Processing Data Splitting Model Training Model Evaluation Model Deployment Monitoring Model Performance Machine Learning Pipeline Tools Machine Learning Pipeline Deployment on Different Platforms FAQs What tools exist for managing data science and machine learning pipelines?

Machine Learning 52

Machine Learning Building Amazon Web Services AWS 52

Ascend.io

JUNE 21, 2023

As a result, each company reinvents its own modern data stack , buying several of these narrow tools and hiring data engineering specialists to integrate them with custom code. This intelligence is also the foundation to understanding the costs of individual data products.

Pipeline-centric 52

Pipeline-centric Data Pipeline Metadata Data 52

Maxime Beauchemin

JANUARY 20, 2017

In retrospect, complex SCD modeling techniques are not intuitive and reduce accessibility. It also becomes the role of the data engineering team to be a “center of excellence” through the definitions of standards, best practices and certification processes for data objects.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

Silectis

JANUARY 3, 2021

Data Catalog An organized inventory of data assets relying on metadata to help with data management. Data Engineering Data engineering is a process by which data engineers make data useful. Machine learning (ML) ML generally refers to algorithms built to identify patterns in big data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Knowledge Hut

SEPTEMBER 29, 2023

Training: Joining the online KnowledgeHut microsoft data engineer certification training that business experts offer is the ideal approach to get ready for the DP-203 data engineering on Microsoft Azure exam. You can browse the data lake files with the interactive training material.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Monte Carlo

JULY 10, 2023

Accuracy reflects the degree to which the data correctly describes the “real-world” objects being described. For example, let’s say a streaming provider has 10 million overall subscribers who can access its content. According to the CRM’s data set, the streaming provider has 13 million subscribers.

Data Collection 40

Data Collection Data Governance Government Data 40

Snowflake

NOVEMBER 29, 2023

In medicine, lower sequencing costs and improved clinical access to NGS technology has been shown to increase diagnostic yield for a range of diseases, from relatively well-understood Mendelian disorders, including muscular dystrophy and epilepsy , to rare diseases such as Alagille syndrome.

Metadata 85

Metadata Healthcare Medical Data Storage 85

Zalando Engineering

OCTOBER 23, 2017

Quality - Validate product data and ensuring it is complete, correct and consistent. This is done in conjunction with our Fashion Librarians (data stewardships) curation guidelines. Accessibility - Ease of data onboarding, and data exposure and consumption are cornerstones of SPP. What color is it?

Media 40

Media Manufacturing Data Ingestion Metadata 40

Confluent

FEBRUARY 21, 2019

Which one you use depends on the particular use to which you are putting the data. A stream is an unbounded sequence of data records ordered by time that represents the past and the present state of data ingested into a Kafka topic. Tables, on the other hand, represent the current state of data records.

Process 80

Process Kafka Datasets SQL 80

Confluent

MAY 7, 2019

Although it is the simplest way to subscribe to and access events from Kafka, behind the scenes, Kafka consumers handle tricky distributed systems challenges like data consistency, failover and load balancing. There is no way that one computer node will ever be able to ingest and process all the events that get generated in real time.

Kafka 111

Kafka Accessible Accessibility Metadata 111

Snowflake

FEBRUARY 13, 2024

Snowpark Updates Model management with the Snowpark Model Registry – public preview Snowpark Model Registry is an integrated solution to register, manage and use models and their metadata natively in Snowflake. Learn more here. This improves manageability, troubleshooting and auditability for security admins. Learn more here.

Data Ingestion 88

Data Ingestion AWS Python Metadata 88

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. Only metadata will be regenerated.

Cloud 77

Cloud Metadata Google Cloud Data Warehouse 77

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. How Apache Iceberg tables structure metadata. I think it’s safe to say it’s getting pretty cold in here. Image courtesy of Dremio. So, is Iceberg right for you?

Data Lake 64

Data Lake Metadata Data Warehouse SQL 64

ProjectPro

FEBRUARY 8, 2023

When Glue receives a trigger, it collects the data, transforms it using code that Glue generates automatically, and then loads it into Amazon S3 or Amazon Redshift. Then, Glue writes the job's metadata into the embedded AWS Glue Data Catalog. being data exactly matches the classifier, and 0.0 Why Use AWS Glue?

AWS 98

AWS Scala Metadata Data Lake 98

Cloudera

DECEMBER 20, 2021

The right set of tools helps businesses utilize data to drive insights and value. But balancing a strong layer of security and governance with easy access to data for all users is no easy task. Winner of the Data Impact Awards 2021: Security & Governance Leadership. You can become a data hero too.

Government 86

Government Data Security Banking Metadata 86

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

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 87

Building Metadata Transportation Data Ingestion 87

Towards Data Science

MARCH 5, 2024

With on-demand pricing, you will generally have access to up to 2000 concurrent slots, shared among all queries in a single project, which is more than enough in most cases. Choosing the right model depends on your data access patterns and compression capabilities. Data can easily be uploaded and stored for low costs.

Bytes 72

Bytes Google Cloud Cloud Storage Utilities 72

Cloudera

SEPTEMBER 9, 2022

The promise of a modern data lakehouse architecture. Imagine having self-service access to all business data, anywhere it may be, and being able to explore it all at once. Imagine quickly answering burning business questions nearly instantly, without waiting for data to be found, shared, and ingested.

Architecture 97

Architecture Metadata Unstructured Data Machine Learning 97

Ascend.io

FEBRUARY 6, 2023

This database then hosts the workloads for the Data Flows in that Data Service. As a result, you can distribute workloads across more infrastructure, and align your Ascend teams with their access to different Snowflake resources. Now we add Data Plane configuration for Databricks and BigQuery.

Cloud 52

Cloud Data Ingestion Raw Data Data Pipeline 52

Ascend.io

FEBRUARY 6, 2023

This database then hosts the workloads for the Data Flows in that Data Service. As a result, you can distribute workloads across more infrastructure, and align your Ascend teams with their access to different Snowflake resources. Now we add Data Plane configuration for Databricks and BigQuery.

Cloud 52

Cloud Data Ingestion Raw Data Data Pipeline 52

Towards Data Science

JULY 24, 2023

Closely related to this is how those same platforms are bundling or unbundling related data services from data ingestion and transformation to data governance and monitoring. Why are these things related, and more importantly, why should data leaders care? Take data governance for example.

Metadata 97

Metadata Data Warehouse Data Lake Data Science 97

Monte Carlo

JUNE 14, 2023

This frequently involves, in some order, extraction (from a source system), transformation (where data is combined with other data and put into the desired format), and loading (into storage where it can be accessed). Most organizations deploy some or all of these data pipeline architectures.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52