Cloudera

AUGUST 26, 2021

While we walk through the steps one by one from data ingestion to analysis, we will also demonstrate how Ozone can serve as an ‘S3’ compatible object store. Learn more about the impacts of global data sharing in this blog, The Ethics of Data Exchange. Data ingestion through ‘s3’. Ozone Namespace Overview.

Data Science 115

Data Science Cloud Hadoop Metadata 115

Towards Data Science

DECEMBER 1, 2023

Now you can easily retrieve it from your account by leveraging “data movement platforms” like Fivetran. This tool automates ELT (Extract, Load, Transform) process, integrating your data from the source system of Google Calendar to our Snowflake data warehouse. There are two ways to access dbt: dbt Cloud and dbt Core.

Data Engineering 69

Data Engineering Data Engineer Project Engineering 69

Rockset

APRIL 18, 2023

We’re excited to introduce vector search on Rockset to power fast and efficient search experiences, personalization engines, fraud detection systems and more. Vector search has seen an explosion in popularity due to improvements in accuracy and broadened accessibility to the models used to generate embeddings.

Unstructured Data 105

Unstructured Data Metadata Machine Learning SQL 105

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

Data Engineering Weekly

OCTOBER 30, 2022

Luis Velasco: Data Contracts - The Mesh Glue I'm excited about the Schemata & Data contract because it perfectly stitches catalogs, data quality, and glossary. The current state of these systems is inherently passive systems.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Ingestion 52

Databand.ai

JUNE 28, 2023

“Observability” has become a bit of a buzzword so it’s probably best to define it: Data observability is the blanket term for monitoring and improving the health of data within applications and systems like data pipelines. Data observability vs. monitoring: what is the difference?

Data Pipeline 98

Data Pipeline Data Engineering Data Engineer Engineering 98

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 132

Cloud Kafka Professional Services Metadata 132

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 85

Data Warehouse Database-centric Metadata Cloud 85

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 72

Data Warehouse Cloud Kafka Cloud Storage 72

Silectis

JANUARY 3, 2021

” Artificial Intelligence AI is a broad term used to describe engineered systems that have been taught to do a task that typically requires human intelligence. BI (Business Intelligence) Strategies and systems used by enterprises to conduct data analysis and make pertinent business decisions.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Maxime Beauchemin

JANUARY 20, 2017

In relation to previously existing roles , the data engineering field could be thought of as a superset of business intelligence and data warehousing that brings more elements from software engineering. Those systems have been taught to normalize the data for storage on their own.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

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. The answer here is to relieve the team of building and maintaining complex systems by adopting a unified end-to-end data pipeline platform.

Pipeline-centric 52

Pipeline-centric Data Pipeline Metadata Data 52

Knowledge Hut

SEPTEMBER 29, 2023

The Azure Data Engineer Certification test evaluates one's capacity for organizing and putting into practice data processing, security, and storage, as well as their capacity for keeping track of and maximizing data processing and storage. Additionally, Apache Spark can be used to learn ingestion methods.

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

Confluent

FEBRUARY 21, 2019

One thing to note is that when the Create method is used on a remote system, the method is run under a host process named Wmiprvse.exe. Which one you use depends on the particular use to which you are putting the data. One can access a stream from the beginning of its time all the way to the most recently recorded values.

Process 80

Process Kafka Datasets SQL 80

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 84

Metadata Healthcare Medical Data Storage 84

Zalando Engineering

OCTOBER 23, 2017

Smart Products Smart Product Platform (SPP) is Zalando’s new product platform which enables the collection, management and exposure of fashion product data across the organization, at scale. Quality - Validate product data and ensuring it is complete, correct and consistent. What color is it? What is it made of?)

Media 40

Media Manufacturing Data Ingestion Metadata 40

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 87

Data Ingestion AWS Python Metadata 87

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. Data processing requirements. We therefore need a way of splitting up the data ingestion work.

Kafka 111

Kafka Accessible Accessibility Metadata 111

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 93

Government Data Security Banking Metadata 93

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

Finally, imagine yourself in the role of a data platform reliability engineer tasked with providing advanced lead time to data pipeline (ETL) owners by proactively identifying issues upstream to their ETL jobs. Let’s review a few of these principles: Ensure data integrity ?—?Accurately Enable seamless integration?—?

Building 87

Building Metadata Transportation Data Ingestion 87

LinkedIn Engineering

JULY 27, 2023

It’s now much easier to manage large infra requirements that have traditionally demanded an amalgamation of teams like DBA, Infra-SRE, Onprem-SMEs, network managers, and access control managers working together. The client system can utilize this package and invoke the workflow methods with provider implementations.

Metadata 75

Metadata Utilities Cloud Data Lake 75

phData: Data Engineering

AUGUST 4, 2023

Data storage is a vital aspect of any Snowflake Data Cloud database. Within Snowflake, data can either be stored locally or accessed from other cloud storage systems. Snowflake hides user data objects and makes them accessible only through SQL queries through the compute layer.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

Towards Data Science

MARCH 5, 2024

The storage system is using Capacitor, a proprietary columnar storage format by Google for semi-structured data and the file system underneath is Colossus, the distributed file system by Google. Choosing the right model depends on your data access patterns and compression capabilities.

Bytes 69

Bytes Google Cloud Cloud Storage Utilities 69

Ascend.io

FEBRUARY 6, 2023

Data Services Listing with BigQuery, Snowflake, and Databricks. Data Service – is a group of Data Flows. At this level, users configure team members, connections to other systems, and event notifications. Data Flow – is an individual data pipeline.

Cloud 52

Cloud Data Ingestion Raw Data Data Pipeline 52

Ascend.io

FEBRUARY 6, 2023

Data Services Listing with BigQuery, Snowflake, and Databricks. Data Service – is a group of Data Flows. At this level, users configure team members, connections to other systems, and event notifications. Data Flow – is an individual data pipeline.

Cloud 52

Cloud Data Ingestion Raw Data Data Pipeline 52

Monte Carlo

JUNE 14, 2023

Data pipeline architecture is the process of designing how data is surfaced from its source system to the consumption layer. No copy data sharing is another emerging data pipeline architecture being pioneered by Snowflake ( Secure Data Sharing ) and Databricks ( Delta Sharing ).

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Rockset

JUNE 6, 2023

This improves efficiency and elasticity, but is challenging to implement for a real-time system. Real-time systems such as Elasticsearch were designed to work off of directly attached storage to allow for fast access in the face of real-time updates. We can load larger datasets by just scaling the storage capacity.

Metadata 52

Metadata Datasets Architecture Algorithm 52

DataKitchen

SEPTEMBER 21, 2023

In the contemporary data landscape, data teams commonly utilize data warehouses or lakes to arrange their data into L1, L2, and L3 layers. These layers help teams delineate different stages of data processing, storage, and access, offering a structured approach to data management. What is Data in Use?

Raw Data 67

Raw Data Data Business Intelligence High Quality Data 67

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 91

Data Lake Business Intelligence Metadata Data Warehouse 91

AltexSoft

JUNE 26, 2023

Read our article on Hotel Data Management to have a full picture of what information can be collected to boost revenue and customer satisfaction in hospitality. While all three are about data acquisition, they have distinct differences. Data integration , on the other hand, happens later in the data management flow.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Ascend.io

JUNE 8, 2023

Acting as the core infrastructure, data pipelines include the crucial steps of data ingestion, transformation, and sharing. Data Ingestion Data in today’s businesses come from an array of sources, including various clouds, APIs, warehouses, and applications.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Ascend.io

MARCH 14, 2023

Data represents our present and our future, and therein lies a significant problem: the more data you’re dealing with, the more challenging it will be to scale your company in a sustainable and standardized way. A monolithic data platform stores all this data in one place. So, what’s the solution?

Government 52

Government Architecture Data Lake Data 52

Ascend.io

MARCH 14, 2023

Data represents our present and our future, and therein lies a significant problem: the more data you’re dealing with, the more challenging it will be to scale your company in a sustainable and standardized way. A monolithic data platform stores all this data in one place. So, what’s the solution?

Government 52

Government Architecture Data Lake Data 52