RandomTrees

FEBRUARY 6, 2024

Generative AI with Data Lineage: By automating the process of collecting lineage metadata, generating visualizations of data lineage, and identifying and troubleshooting data lineage problems. Generating new insights: Ability to generate new insights from data that would not be possible to find using traditional methods.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Monte Carlo

FEBRUARY 9, 2023

Over the past several years, data warehouses have evolved dramatically, but that doesn’t mean the fundamentals underpinning sound data architecture needs to be thrown out the window. What is a Data Vault model? The data vault paradigm addresses the desire to overlay organization on top of semi-permanent raw data storage.

Architecture 52

Architecture Raw Data Metadata Data Warehouse 52

Monte Carlo

MARCH 28, 2023

As part of this movement, Fivetran and dbt fundamentally altered the data pipeline from ETL to ELT. Hightouch interrupted SaaS eating the world in an attempt to shift the center of gravity to the data warehouse. Other common light transformations done within the ingestion phase are data formatting and deduplication.

Data Warehouse 126

Data Warehouse Raw Data Data Pipeline Software Engineer 126

Snowflake

NOVEMBER 30, 2023

At TCS , we help companies shift their enterprise data warehouse (EDW) platforms to the cloud as well as offering IT services. We’re extremely familiar with just how tricky a cloud migration can be, especially when it involves moving historical business data. How many tables and views will be migrated, and how much raw data?

Data Warehouse 98

Data Warehouse Banking Data Cloud 98

AltexSoft

AUGUST 29, 2023

The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. This article explains what a data lake is, its architecture, and diverse use cases. Data warehouse vs. data lake in a nutshell.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Ascend.io

AUGUST 31, 2023

Secondly , the rise of data lakes that catalyzed the transition from ELT to ELT and paved the way for niche paradigms such as Reverse ETL and Zero-ETL. Still, these methods have been overshadowed by EtLT — the predominant approach reshaping today’s data landscape. Read More: What is ETL?

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Towards Data Science

FEBRUARY 9, 2024

Data Store Another significant change from 2021 to 2024 lies in the shift from “Data Warehouse” to “Data Store,” acknowledging the expanding database horizon, including the rise of Data Lakes. Their robust core offering seamlessly integrates data warehouses with data-hungry applications.

Building 82

Building Transportation Data Lake Metadata 82

Data Engineering Podcast

DECEMBER 16, 2019

The solution to discoverability and tracking of data lineage is to incorporate a metadata repository into your data platform. The metadata repository serves as a data catalog and a means of reporting on the health and status of your datasets when it is properly integrated into the rest of your tools.

Metadata 100

Metadata PostgreSQL Datasets Data Warehouse 100

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. It is designed to be easily queryable with SQL even for large analytic tables (we’re talking petabytes of data). How Apache Iceberg tables structure metadata.

Data Lake 64

Data Lake Metadata Data Warehouse SQL 64

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. See our post: Data Lakes vs. Data Warehouses.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

ProjectPro

FEBRUARY 16, 2023

With so much riding on the efficiency of ETL processes for data engineering teams, it is essential to take a deep dive into the complex world of ETL on AWS to take your data management to the next level. ETL has typically been carried out utilizing data warehouses and on-premise ETL tools.

AWS 52

AWS Data Management ETL Tools Management 52

Towards Data Science

APRIL 3, 2023

As part of this movement, Fivetran and dbt fundamentally altered the data pipeline from ETL to ELT. Hightouch interrupted SaaS eating the world in an attempt to shift the center of gravity to the data warehouse. Other common light transformations done within the ingestion phase are data formatting and deduplication.

Data Engineering 98

Data Engineering Data Engineer Engineering Data Warehouse 98

AltexSoft

NOVEMBER 10, 2021

The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. What is a data lakehouse? Data warehouse vs data lake vs data lakehouse: What’s the difference.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

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

Snowflake

APRIL 5, 2023

Two different data modeling approaches—dimensional data modeling and Data Vault—each have their own pros and cons. Modernizing a data warehouse with Snowflake Data Cloud is a smart investment that can provide significant benefits to businesses of all sizes, today more than ever as data models become ever more complex.

Data Warehouse 52

Data Warehouse Healthcare Unstructured Data Metadata 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. How Does AWS Glue Work?

AWS 98

AWS Scala Metadata Data Lake 98

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

Hepta Analytics

FEBRUARY 14, 2022

This week, we got to think about our data ingestion design. We looked at the following: How do we ingest – ETL vs ELT Where do we store the data – Data lake vs data warehouse Which tool to we use to ingest – cronjob vs workflow engine NOTE : This weeks task requires good internet speed and good compute.

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

AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

IT 59

IT Data Warehouse Data Governance Data Lake 59

DoorDash Engineering

APRIL 12, 2023

As we mentioned in our previous blog , we began with a ‘Bring Your Own SQL’ method, in which data scientists checked in ad-hoc Snowflake (our primary data warehouse) SQL files to create metrics for experiments, and metrics metadata was provided as JSON configs for each experiment.

SQL 82

SQL Metadata Raw Data Government 82

Databand.ai

JULY 28, 2022

Those tasks are saving the extracted data in the data lake (or warehouse or lakehouse). Other tasks, probably SQL jobs orchestrated with DBT, are running transformation on the loaded data. They are querying raw data tables, enriching them, joining between tables, and creating business data – all ready to be used.

Metadata 52

Metadata Data Lake Datasets Data Warehouse 52

Snowflake

MARCH 30, 2023

Collecting, cleaning, and organizing data into a coherent form for business users to consume are all standard data modeling and data engineering tasks for loading a data warehouse. Based on Tecton blog So is this similar to data engineering pipelines into a data lake/warehouse?

Engineering 82

Engineering Raw Data Data Science Scala 82

Ascend.io

APRIL 7, 2023

Source: Data Mesh Principles and Logical Architecture by Zhamak Dehghani What is a Data Fabric? Data fabric is a centralized platform architecture originating from a curated metadata layer that sits on top of an organization’s data infrastructure. Increasing speed.

Metadata 52

Metadata Datasets Data Governance Government 52

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. RudderStack builds your CDP on top of your data warehouse, giving you a more secure and cost-effective solution.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Maxime Beauchemin

AUGUST 28, 2017

Consensus seeking Whether you think that old-school data warehousing concepts are fading or not, the quest to achieve conformed dimensions and conformed metrics is as relevant as it ever was. The data warehouse needs to reflect the business, and the business should have clarity on how it thinks about analytics.

Data Engineering 174

Data Engineering Data Engineer Engineering Software Engineer 174

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

Towards Data Science

DECEMBER 23, 2023

Reading and transforming data with PySpark : With our catch on board, we use PySpark, our compass, to navigate through this sea of data. PySpark helps us clean, organize, and make sense of our catch, transforming raw data into valuable insights, much like how a skilled chef would prepare a variety of dishes from the day’s catch.

SQL 75

SQL Data Analytics Hadoop Raw Data 75

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. Luke: Let’s talk about some of the fundamentals of modern data architecture. What is a data fabric?

Data Lake 79

Data Lake Analytics Application Cloud Storage Architecture 79

Monte Carlo

JANUARY 4, 2024

Data processing : Whatnot data teams rely on Snowflake and dbt for processing, with orchestration in Dagster. His team uses Protobuf, a data definition language, to describe how data should look in a specific format. That schema is then pushed through the common interface. “One

Data 40

Data Metadata Software Engineer Software Engineering 40

AltexSoft

JUNE 26, 2023

Data collection revolves around gathering raw data from various sources, with the objective of using it for analysis and decision-making. It includes manual data entries, online surveys, extracting information from documents and databases, capturing signals from sensors, and more.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

AltexSoft

DECEMBER 23, 2022

It is a data integration process with which you first extract raw information (in its original formats) from various sources and load it straight into a central repository such as a cloud data warehouse , a data lake , or a data lakehouse where you transform it into suitable formats for further analysis and reporting.

Process 52

Process Building Raw Data Data Lake 52

Monte Carlo

AUGUST 19, 2023

With companies moving their data platforms to the cloud, the emergence of cloud-native solutions ( data warehouse vs data lake or even a data lakehouse ) have taken over the market, offering more accessible and affordable options for storing data relative to many on-premises solutions.

Building 52

Building BI Data Lake Data Governance 52

ProjectPro

NOVEMBER 30, 2021

Now that we have understood how much significant role data plays, it opens the way to a set of more questions like How do we acquire or extract raw data from the source? How do we transform this data to get valuable insights from it? Where do we finally store or load the transformed data?

Process 52

Process Data Pipeline Data Warehouse AWS 52

AltexSoft

SEPTEMBER 23, 2021

One of the innovative ways to address this problem is to build a data hub — a platform that unites all your information sources under a single umbrella. This article explains the main concepts of a data hub, its architecture, and how it differs from data warehouses and data lakes. What is Data Hub?

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

Monte Carlo

APRIL 1, 2021

is whether to choose a data warehouse or lake to power storage and compute for their analytics. While data warehouses provide structure that makes it easy for data teams to efficiently operationalize data (i.e., Data discovery tools and platforms can help. Image courtesy of Adrian on Unsplash.

Data Lake 52

Data Lake Unstructured Data Data Warehouse Metadata 52

Maxime Beauchemin

JANUARY 7, 2018

While business rules evolve constantly, and while corrections and adjustments to the process are more the rule than the exception, it’s important to insulate compute logic changes from data changes and have control over all of the moving parts. But how do we model this in a functional data warehouse without mutating data?

Data Engineering 183

Data Engineering Data Engineer Data Process Process 183