Snowflake

NOVEMBER 29, 2023

For example, the data storage systems and processing pipelines that capture information from genomic sequencing instruments are very different from those that capture the clinical characteristics of a patient from a site. Alation, Collibra) to some niche ones Allows easy ingestion of metadata (such as genomics metadata in Fig.

Metadata 84

Metadata Healthcare Medical Data Storage 84

Data Engineering Podcast

JUNE 5, 2022

Summary The best way to make sure that you don’t leak sensitive data is to never have it in the first place. The team at Skyflow decided that the second best way is to build a storage system dedicated to securely managing your sensitive information and making it easy to integrate with your applications and data systems.

Data Security 100

Data Security Metadata MongoDB Scala 100

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. Learn more here. The pipe can only be set to this state by Snowflake Support.

Data Ingestion 87

Data Ingestion AWS Python Metadata 87

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

phData: Data Engineering

SEPTEMBER 19, 2023

This blog will guide you through the best data modeling methodologies and processes for your data lake, helping you make informed decisions and optimize your data management practices. What is a Data Lake? What are Data Modeling Methodologies, and Why Are They Important for a Data Lake?

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Monte Carlo

JANUARY 5, 2024

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. Table of Contents What is data lakehouse architecture? The 5 key layers of data lakehouse architecture 1. Ingestion layer 2. Metadata layer 4. API layer 5.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. Table of Contents What is data lakehouse architecture? The 5 key layers of data lakehouse architecture 1. Ingestion layer 2. Metadata layer 4. API layer 5.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

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 111

Data Science Cloud Hadoop Metadata 111

Databand.ai

AUGUST 30, 2023

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows. Data Sources Data sources are the backbone of any DataOps architecture.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Christophe Blefari

JANUARY 20, 2024

What is data engineering As I said it before data engineering is still a young discipline with many different definitions. Still, we can have a common ground when mixing software engineering, DevOps principles, Cloud — or on-prem — systems understanding and data literacy. Is it really modern?

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

Precisely

SEPTEMBER 11, 2023

By 2025, 80% of mainstream data quality vendors will expand their product capabilities to provide greater data insights by discovering patterns, trends, data relationships, and error resolution. Context-based bots expedite information retrieval from documentation, knowledge bases, or metadata.

Metadata 52

Metadata Data Integration Datasets Data Analysis Tools 52

DataKitchen

OCTOBER 20, 2023

By offering real-time tracking mechanisms and sending targeted alerts to specific consumers, a Payload DJ can immediately notify them of any changes, delays, or issues affecting their data. This transparent system effectively answers real-time data location and status questions, thus enhancing customer trust and satisfaction.

Insurance 67

Insurance Pharmaceutical Data Data Ingestion 67

Monte Carlo

APRIL 4, 2023

The modern data stack consists of multiple interdependent data systems all working together. Data anomalies and incidents can be introduced across any point of this pipeline, which can make root cause analysis a fragmented and frustrating multi-tab process. Was it a query changed within Snowflake? A modified dbt model?

BI 64

BI Data Ingestion Metadata Data Pipeline 64

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

Cloudera

DECEMBER 20, 2021

Understanding that the future of banking is data-driven and cloud-based, Bank of the West embraced cloud computing and its benefits, like remote capabilities, integrated processes, and flexible systems. Winner of the Data Impact Awards 2021: Security & Governance Leadership. You can become a data hero too.

Government 89

Government Data Security Banking Metadata 89

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 the primary system requirements that have influenced the design choices? In fact, while only 3.5%

Machine Learning 100

Machine Learning Database MySQL PostgreSQL 100

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

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. Feature Generation: Transform and aggregate data during the ingest process to generate complex features and reduce data storage volumes.

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

LinkedIn Engineering

DECEMBER 12, 2023

Although we can prescribe a way to achieve an overall privacy guarantee with differential privacy with various algorithms, there is still the challenge of integrating differential privacy into an existing system that can return analytics for streaming data in real-time under intense query loads.

Algorithm 77

Algorithm Metadata SQL Datasets 77

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

Data Engineering Weekly

MAY 16, 2023

In the first part of this series, we talked about design patterns for data creation and the pros & cons of each system from the data contract perspective. In the second part, we will focus on architectural patterns to implement data quality from a data contract perspective. Why is Data Quality Expensive?

Engineering 110

Engineering Kafka Data Pipeline Data Warehouse 110

Cloudera

JUNE 16, 2022

Faster data ingestion: streaming ingestion pipelines. Reduce ingest latency and complexity: Multiple point solutions were needed to move data from different data sources to downstream systems. Faster ingestion was needed to reduce overall analytics latency. .

Kafka 86

Kafka Manufacturing Data Lake SQL 86

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 86

Data Lake Business Intelligence Metadata Data Warehouse 86

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. As of now, Fivetran offers 14-day free trial —  link.

Data Engineering 70

Data Engineering Data Engineer Project Engineering 70

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

Rockset

AUGUST 28, 2023

Query across your ANN indexes on vector embeddings, and your JSON and geospatial “metadata” fields efficiently. Spin a Virtual Instance for streaming data ingestion. As AI models become more advanced, LLMs and generative AI apps are liberating information that is typically locked up in unstructured data.

Metadata 78

Metadata Unstructured Data SQL Database 78

Cloudera

OCTOBER 11, 2021

The platform converges data cataloging, data ingestion, data profiling, data tagging, data discovery, and data exploration into a unified platform, driven by metadata. Modak Nabu automates repetitive tasks in the data preparation process and thus accelerates the data preparation by 4x.

Data Engineering 92

Data Engineering Data Engineer Cloud Engineering 92

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

LinkedIn Engineering

JULY 27, 2023

Costwiz application and workflow management system For the initial rollout, we built a single-page app for users to perform actions on their recommendations (as seen in Figure 1). Data providers: The core input data for which the workflows should be executed and other supporting data required are fed to the system by data providers.

Metadata 75

Metadata Utilities Cloud Data Lake 75

Ascend.io

MAY 8, 2023

Scheduling Data Ingestion Most source systems for your data have an inherent update cycle. Some produce change log data for every new record, others produce file drops into a cloud bucket every 15 minutes, and others run batch exports nightly. Scheduling refresh of data ingestion for each data source.

Data Pipeline 52

Data Pipeline Data Ingestion Metadata Data 52

Cloudera

APRIL 22, 2021

This CVD is built using Cloudera Data Platform Private Cloud Base 7.1.5 on Cisco UCS S3260 M5 Rack Server with Apache Ozone as the distributed file system for CDP. Collects and aggregates metadata from components and present cluster state. Metadata in cluster is disjoint across components. Data Generation at Scale.

Pipeline-centric 100

Pipeline-centric Data Lake Hadoop Metadata 100

Netflix Tech

OCTOBER 19, 2020

which is difficult when troubleshooting distributed systems. We could also get contextual information about the streaming session by joining relevant traces with account metadata and service logs. Edgar uses this infrastructure tagging schema to query and join traces with log data for troubleshooting streaming sessions.

Building 93

Building Transportation Metadata Java 93

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

Netflix Tech

DECEMBER 21, 2020

We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits. Use cases We found several use cases where a system like AutoOptimize can bring tons of value. We can also reorganize the metadata to make file scanning much faster.

Data Warehouse 81

Data Warehouse Metadata Algorithm Data 81

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