Snowflake

JUNE 4, 2024

Iceberg tables (now generally available), when combined with the capabilities of the Snowflake platform, allow you to build various open architectures, including a data lakehouse and data mesh. Parquet Direct (private preview) allows you to use Iceberg without rewriting or duplicating Parquet files — even as new Parquet files arrive.

Government 84

Government Data Ingestion Data PostgreSQL 84

Precisely

MAY 13, 2024

A true enterprise-grade integration solution calls for source and target connectors that can accommodate: VSAM files COBOL copybooks open standards like JSON modern platforms like Amazon Web Services ( AWS ), Confluent , Databricks , or Snowflake Questions to ask each vendor: Which enterprise data sources and targets do you support?

Data Integration 52

Data Integration Metadata Amazon Web Services Data Governance 52

Data Engineering Weekly

MARCH 24, 2024

The APIs support emitting unstructured log lines and typed metadata key-value pairs (per line). Ingestion clusters read objects from queues and support additional parsing based on user-defined regex extraction rules. The extracted key-value pairs are written to the line’s metadata.

Data Engineering 62

Data Engineering Data Engineer Engineering Metadata 62

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 89

Data Ingestion AWS Python Metadata 89

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 82

Cloud Metadata Google Cloud Data Warehouse 82

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

Snowflake

NOVEMBER 11, 2024

With Hybrid Tables’ fast, high-concurrency point operations, you can store application and workflow state directly in Snowflake, serve data without reverse ETL and build lightweight transactional apps while maintaining a single governance and security model for both transactional and analytical data — all on one platform.

Data Architecture 82

Data Architecture Architecture Data Lake Kafka 82

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 104

Architecture Metadata Machine Learning Unstructured Data 104

Cloudyard

JULY 16, 2024

Guarantees an action always happens after a task graph (your data pipeline) finishes running, regardless of individual task success or failure. Landing Zone (CUSTOMER_LANDING): This table acts as a landing zone for the incoming data. New Customer: If a new customer record is there, a new row inserts into the CUSTOMER_LANDING table.

Data Ingestion 52

Data Ingestion Data Pipeline Metadata Utilities 52

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

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

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

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

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

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 96

Metadata Data Warehouse Data Lake Data Science 96

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

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 71

Data Engineering Data Engineer Project Engineering 71

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 90

Data Lake Business Intelligence Metadata Data Warehouse 90

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

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 Database 40

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 89

Building Metadata Transportation Data Ingestion 89

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

Edureka

AUGUST 19, 2024

At many levels, the workspace and data are secured by Azure Databricks. Azure Databricks uses Azure security features, such as role-based access control and network security groups, ensuring data remains out of reach from undesired access. Compute Plane: Processes data against serverless or classic compute resources.

Architecture 40

Architecture Data Lake Machine Learning Data Ingestion 40

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

MARCH 31, 2021

Customers who have chosen Google Cloud as their cloud platform can now use CDP Public Cloud to create secure governed data lakes in their own cloud accounts and deliver security, compliance and metadata management across multiple compute clusters. Data Preparation (Apache Spark and Apache Hive) .

Google Cloud 97

Google Cloud Cloud Amazon Web Services Cloud Storage 97

Towards Data Science

JULY 8, 2024

ML Pipeline operations begins with data ingestion and validation, followed by transformation. The transformed data is trained and deployed. You can access it from here. This process also creates a sqlite database for storing the metadata of the pipeline process.

Data Preparation 47

Data Preparation Datasets Metadata Data Ingestion 47

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

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 70

Bytes Google Cloud Cloud Storage Utilities 70

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

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

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. Data connections are secured through Azure Key Vaults and network connectivity is protected by LinkedIn's NACL control.

Metadata 75

Metadata Utilities Cloud Database 75

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

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 87

Machine Learning Algorithm Government Metadata 87

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

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