DataKitchen

AUGUST 9, 2021

The data industry has a wide variety of approaches and philosophies for managing data: Inman data factory, Kimball methodology, s tar schema , or the data vault pattern, which can be a great way to store and organize raw data, and more. Data mesh does not replace or require any of these.

Pharmaceutical 98

Pharmaceutical Raw Data Data Lake Data 98

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

ProjectPro

FEBRUARY 16, 2023

ETL Architecture on AWS: Examining the Scalable Architecture for Data Transformation ETL Architecture on AWS typically consists of three components - Source Data Store A Data Transformation Layer Target Data Store Source Data Store The source data store is where raw data is stored before being transformed and loaded into the target data store.

AWS 52

AWS Data Management ETL Tools Management 52

Ascend.io

AUGUST 31, 2023

Read More: What is ETL? – (Extract, Transform, Load) ELT for the Data Lake Pattern As discussed earlier, data lakes are highly flexible repositories that can store vast volumes of raw data with very little preprocessing. Their task is straightforward: take the raw data and transform it into a structured, coherent format.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Rockset

SEPTEMBER 26, 2023

With easy access to data streams through Rockset’s integration with Confluent Cloud, businesses can: Create a real-time knowledge base for AI applications: Build a shared source of real-time truth for all your operational and analytical data, no matter where it lives for sophisticated model building and fine-tuning.

Metadata 64

Metadata Kafka Cloud Database 64

DataKitchen

MAY 4, 2023

Chris talks about the idea of a ‘domain’ as a principle of Data Mesh. A domain is a unit that includes integrated or raw data, artifacts created from data, the code that acts upon the data, the team responsible for the data, and metadata such as data catalog, lineage, and processing history.

Raw Data 52

Raw Data Data Datasets Metadata 52

Hepta Analytics

FEBRUARY 14, 2022

When the business intelligence needs change, they can go query the raw data again. ELT: source Data Lake vs Data Warehouse Data lake stores raw data. The purpose of the data is not determined. The data is easily accessible and is easy to update. x+ and set minimum memory to 5GB.

Data Ingestion 52

Data Ingestion Data Engineering Data Engineer Engineering 52

Monte Carlo

JUNE 28, 2022

Over the past several years, cloud data lakes like Databricks have gotten so powerful (and popular) that according to Mordor Intelligence , the data lake market is expected to grow from $3.74 Traditionally, data lakes held raw data in its native format and were known for their flexibility, speed, and open source ecosystem.

Data Lake 52

Data Lake Metadata AWS Data Warehouse 52

Snowflake

NOVEMBER 30, 2023

How many tables and views will be migrated, and how much raw data? Are there redundant, unused, temporary or other types of data assets that can be removed to reduce the load? What is the best time to extract the data so it has minimal impact on business operations?

Data Warehouse 99

Data Warehouse Banking Data Cloud 99

Snowflake

APRIL 5, 2023

You can see how Data Vault overcomes some limitations of the dimensional model below: Why Data Vault can be a better choice for CQR and management data warehousing In the CQR, data quality and accuracy are critical. Metadata in the Data Vault approach helps to track the origin and processing of data.

Data Warehouse 52

Data Warehouse Healthcare Unstructured Data Metadata 52

Databand.ai

AUGUST 30, 2023

In a DataOps architecture, it’s crucial to have an efficient and scalable data ingestion process that can handle data from diverse sources and formats. This requires implementing robust data integration tools and practices, such as data validation, data cleansing, and metadata management.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Grouparoo

JANUARY 11, 2022

A data warehouse is a unified repository where data from diverse sources undergo aggregation and integration into a usable source of information. To achieve this, a data warehouse will require processes to gather and integrate data, manage data quality, create metadata, and support any regulatory compliance and governance procedures.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Rockset

AUGUST 2, 2023

All of these assessments go back to the AI insights initiative that led Windward to re-examine its data stack. The steps Windward takes to create proprietary data and AI insights As Windward operated in a batch-based data stack, they stored raw data in S3.

Database-centric 52

Database-centric PostgreSQL Transportation Insurance 52

Workfall

AUGUST 14, 2023

DBT, which stands for Data Build Tool, is a powerful tool designed to transform and manage data in a scalable and reproducible manner. It allows data engineers to define and execute data transformations in a structured and modular way. Initialize the Airflow metadata database by running airflow initdb in your terminal.

Data Pipeline 62

Data Pipeline Raw Data Data Database 62

Cloudera

SEPTEMBER 15, 2022

The data products are packaged around the business needs and in support of the business use cases. This step requires curation, harmonization, and standardization from the raw data into the products.

Data Lake 79

Data Lake Analytics Application Cloud Storage Architecture 79

AltexSoft

AUGUST 3, 2021

Now, the primary function of data labeling is tagging objects on raw data to help the ML model make accurate predictions and estimations. That said, data annotation is key in training ML models if you want to achieve high-quality outputs. Explaining Data Annotation for ML.

Datasets 59

Datasets High Quality Data Metadata Raw Data 59

ProjectPro

FEBRUARY 25, 2022

Each stage of the data pipeline passes processed data to the next step, i.e., it gives the output of one phase as input data into the next phase. Data Preprocessing- This step entails collecting raw and inconsistent data selected by a team of experts.

Machine Learning 52

Machine Learning Building Amazon Web Services AWS 52

Knowledge Hut

NOVEMBER 28, 2023

Data Pipelines Data lakes continue to get new names in the same year, and it becomes imperative for data engineers to supplement their skills with data pipelines that help them work comprehensively with real-time streams, daily occurrence raw data, and data warehouse queries.

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

Rockset

JANUARY 28, 2020

Aside from video data from each camera-equipped store, Standard deals with other data sets such as transactional data, store inventory data that arrive in different formats from different retailers, and metadata derived from the extensive video captured by their cameras.

Retail 40

Retail Google Cloud Raw Data Data Lake 40

Ascend.io

APRIL 12, 2023

Field and column names, data types, and variations in delimiters that designate fields. It should detect “schema drift,” and may involve operations that validate datasets against source system metadata, for example. For starters, we must acknowledge that to make your data usable, you have to process it. In the correct storage.

Data Pipeline 52

Data Pipeline Data Integration Datasets Data 52

dbt Developer Hub

FEBRUARY 12, 2024

dbt Explorer centralizes documentation, lineage, and execution metadata to reduce the work required to ship trusted data products faster. Knowing data lineage inherently increases your level of trust in the reporting you use to make the right decisions. Enter dbt Explorer ! Look at that lineage!

Metadata 59

Metadata Raw Data BI Project 59

Monte Carlo

FEBRUARY 9, 2023

For those unfamiliar, data vault is a data warehouse modeling methodology created by Dan Linstedt (you may be familiar with Kimball or Imon models ) created in 2000 and updated in 2013. Data vault collects and organizes raw data as underlying structure to act as the source to feed Kimball or Inmon dimensional models.

Architecture 52

Architecture Raw Data Metadata Data Warehouse 52

ProjectPro

OCTOBER 15, 2014

Hive- Performance Benchmarking Hive vs Pig Pig vs Hive - Differences Pig Hive Procedural Data Flow Language Declarative SQLish Language For Programming For creating reports Mainly used by Researchers and Programmers Mainly used by Data Analysts Operates on the client side of a cluster. Does not have a dedicated metadata database.

Hadoop 52

Hadoop Unstructured Data Java SQL 52

Precisely

OCTOBER 5, 2023

According to the 2023 Data Integrity Trends and Insights Report , published in partnership between Precisely and Drexel University’s LeBow College of Business, 77% of data and analytics professionals say data-driven decision-making is the top goal of their data programs. That’s where data enrichment comes in.

Raw Data 69

Raw Data Insurance Datasets Telecommunication 69

Monte Carlo

AUGUST 25, 2023

One advantage of data warehouses is their integrated nature. As fully managed solutions, data warehouses are designed to offer ease of construction and operation. A warehouse can be a one-stop solution, where metadata, storage, and compute components come from the same place and are under the orchestration of a single vendor.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw 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. 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

Monte Carlo

JULY 25, 2023

What is data curation? Data curation is the process of transforming and enriching larger amounts of raw data into smaller, more widely accessible subsets of data that provide additional value to the organization or the intended use case. It would also make the data engineering team a bottleneck.

Raw Data 52

Raw Data Data Warehouse Data Architecture 52

Monte Carlo

MAY 30, 2023

It’s designed to improve upon the performance and usability challenges of older data storage formats such as Apache Hive and Apache Parquet. For example, Monte Carlo can monitor Apache Iceberg tables for data quality incidents, where other data observability platforms may be more limited.

Metadata 52

Metadata Raw Data Data Lake Data 52

ProjectPro

FEBRUARY 8, 2023

But this data is not that easy to manage since a lot of the data that we produce today is unstructured. In fact, 95% of organizations acknowledge the need to manage unstructured raw data since it is challenging and expensive to manage and analyze, which makes it a major concern for most businesses. Why Use AWS Glue?

AWS 98

AWS Scala Metadata Data Lake 98

dbt Developer Hub

NOVEMBER 28, 2021

This could just as easily have been Snowflake or Redshift, but I chose BigQuery because one of my data sources is already there as a public dataset. dbt seeds data from offline sources and performs necessary transformations on data after it's been loaded into BigQuery. Let's dig into each data source one at a time.

Raw Data 52

Raw Data Metadata Database Datasets 52

Towards Data Science

FEBRUARY 9, 2024

Selecting the right data store solution for each aspect of the Data Lake is crucial, but the overarching technology decision involves tying together and exploring these stores to transform raw data into downstream insights. This metadata is then utilized to manage, monitor, and foster the growth of the platform.

Building 82

Building Transportation Data Lake Metadata 82

Cloudyard

MARCH 4, 2023

Secondly, Define Business Rules : Develop the transformation on RAW data and include the Business logic. Develop the relationship among different sources table to produce meaningful data. Thirdly, Data Consumption: Develop the Views on Transformed or aggregated tables. Snowpipe to automate the ingestion process.

Architecture 52

Architecture Cloud Metadata Data Ingestion 52

DataKitchen

SEPTEMBER 21, 2023

The current landscape of Data Observability Tools shows a marked focus on “Data in Place,” leaving a significant gap in the “Data in Use.” ” When monitoring raw data, these tools often excel, offering complete standard data checks that automate much of the data validation process.

Raw Data 67

Raw Data Data Business Intelligence High Quality Data 67

dbt Developer Hub

DECEMBER 5, 2021

We’ve also included some sample raw data to add to your warehouse so you can run these projects yourself! We've also leveled up to using incremental logic for our largest data sets to speed up our runs and deliver insights faster. Advanced use of metadata Themes and Goals In adulthood, we're turning our gaze even further inward.

Project 52

Project Building Metadata BI 52

Knowledge Hut

DECEMBER 29, 2023

The “head” tags (<head> and </head>) contain the metadata or information about the website. Not all of the metadata is visible on the website, some of them are information for the browsers. SSG is a tool that generates HTML websites using a set of templates and raw data.

Portfolio 98

Portfolio Amazon Web Services Programming Language Coding 98

Data Engineering Weekly

JANUARY 15, 2023

SiliconANGLE theCUBE: Analyst Predictions 2023 - The Future of Data Management By far one of the best analyses of trends in Data Management. 2023 predictions from the panel are; Unified metadata becomes kingmaker. The names hold less meaning to the outcome, but its fancy. link] All rights reserved ProtoGrowth Inc, India.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

DataKitchen

FEBRUARY 2, 2023

Most data governance tools today start with the slow, waterfall building of metadata with data stewards and then hope to use that metadata to drive code that runs in production. In reality, the ‘active metadata’ is just a written specification for a data developer to write their code.

Metadata 52

Metadata BI Government ETL Tools 52