Cloudera

JANUARY 20, 2021

Most of what is written though has to do with the enabling technology platforms (cloud or edge or point solutions like data warehouses) or use cases that are driving these benefits (predictive analytics applied to preventive maintenance, financial institution’s fraud detection, or predictive health monitoring as examples) not the underlying data.

Manufacturing 103

Manufacturing Data Warehouse Kafka Retail 103

Cloudera

DECEMBER 21, 2020

Company data exists in the data lake. Data Catalog profilers have been run on existing databases in the Data Lake. A Cloudera Data Warehouse virtual warehouse with Cloudera Data Visualisation enabled exists. A Cloudera Data Engineering service exists. The Data Scientist.

Banking 97

Banking Data Lake Data Data Warehouse 97

Data Engineering Podcast

APRIL 19, 2021

Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. RudderStack’s smart customer data pipeline is warehouse-first.

Data Pipeline 100

Data Pipeline Machine Learning Data Warehouse Datasets 100

Grouparoo

MARCH 30, 2021

Teams are centralizing their data in their data warehouse by loading data in and transforming it as necessary. sql files that, when run in the right order, create useful rollup tables or materialized views of the data. Increasingly, we are seeing teams turn to dbt to do this transforming. The idea is to write *.sql

Data Warehouse 52

Data Warehouse SQL Project Database 52

Data Engineering Podcast

DECEMBER 21, 2020

Fortunately, there’s hope: in the same way that New Relic, DataDog, and other Application Performance Management solutions ensure reliable software and keep application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. What are the complexities that creep into data masking?

Data Governance 130

Data Governance Government Data Lake Banking 130

Knowledge Hut

MARCH 13, 2024

Big Data Analyst Big data engineers can also become influential data analysts skilled at handling large datasets, structured or unstructured. Data Warehouse Engineer Along with developing data systems, a big data engineer’s roles and responsibilities include maintaining those systems.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

Knowledge Hut

MARCH 13, 2024

Big Data Analyst Big data engineers can also become influential data analysts skilled at handling large datasets, structured or unstructured. Data Warehouse Engineer Along with developing data systems, a big data engineer’s roles and responsibilities include maintaining those systems.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

Striim

JANUARY 4, 2023

Companies need to analyze large volumes of datasets, leading to an increase in data producers and consumers within their IT infrastructures. These companies collect data from production applications and B2B SaaS tools (e.g., This data makes its way into a data repository, like a data warehouse (e.g.,

PostgreSQL 52

PostgreSQL Data Warehouse Data Lake Data 52

dbt Developer Hub

JULY 2, 2023

The Data Vault data model is designed using a few well-applied principles, and in practice, pools source data so it is available for use by all downstream consumers. This promotes a scalable data warehouse through reusability and modularity. Should you consider Data Vault for your data warehouse?

Cloud 52

Cloud Data Warehouse Data BI 52

phData: Data Engineering

OCTOBER 12, 2022

It allows you to tag which final models are being used for a particular data product or dashboard. The great thing about this is that you can tag your final models, and thanks to the DAG, the parent models will all be brought into that exposure. Our team of data experts are happy to assist. Reach out today!

Cloud 52

Cloud Metadata SQL Data Warehouse 52

Data Engineering Weekly

JUNE 4, 2023

[link] Uber: Spark Analysers: Catching Anti-Patterns In Spark Apps One of the challenges in commoditizing data processing engines like Spark is that it requires an expert user to understand and operate this system. However, both techniques test once the transformation task is completed.

Data Engineering 52

Data Engineering Data Engineer Engineering Medical 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

Netflix Tech

OCTOBER 27, 2021

A modern ML stack consists of many independent systems like data warehouses, compute layers, model serving systems, and, in particular, notebooks. They can also tag their runs and filter the view by tags, allowing them to focus on particular subsets of experiments. The GUI should integrate well with other GUIs.

Python 89

Python Machine Learning Data Science Metadata 89

Monte Carlo

APRIL 28, 2022

Here is one example from the guide that shows expected credit consumption from a fictitious company with 8 users storing 4 Terabytes of data and working across two virtual data warehouses: Image from Snowflake pricing guide. That’s because the larger data warehouses also run queries faster.

Data Warehouse 52

Data Warehouse Bytes SQL AWS 52

Monte Carlo

JULY 27, 2023

With data optimization, it can be helpful to breakdown and look at the different components across your intricate system. Typically, the data warehouse, lake, or lakehouse will be a good place to start. Of course, improving visibility into costs often requires a level of discipline and vigor across the data engineering team.

Data 52

Data Architecture Data Warehouse Data Engineering 52

Knowledge Hut

SEPTEMBER 28, 2023

Types of Power BI There are three types of Power BI platforms : Power BI Desktop Power BI Services Power BI Mobile In addition to this, it has a cloud data warehouse to manage the data models for smooth deployment. These key areas provide a solid foundation for acquiring new skills and enhancing existing ones.

BI 52

BI Business Intelligence Certification Raw Data 52

Towards Data Science

JUNE 6, 2023

The fact it’s insanely fast and does (mostly) all processing in memory make it a good choice for building my personal data warehouse. Fitbit activity data The first collection of files I looked at was activity data. Extra points for its seamless integration with Python and R.

Data Warehouse 94

Data Warehouse Data SQL Data Analysis 94

Snowflake

NOVEMBER 2, 2023

Over the years, the technology landscape for data management has given rise to various architecture patterns, each thoughtfully designed to cater to specific use cases and requirements. These patterns include both centralized storage patterns like data warehouse , data lake and data lakehouse , and distributed patterns such as data mesh.

Data Lake 84

Data Lake Data Warehouse Cloud Unstructured Data 84

Monte Carlo

MARCH 30, 2022

Ownership drift If you don’t have visibility across all your data assets, you don’t have visibility into all your data quality issues. Photo by krakenimages on Unsplash A data warehouse or lake is an absolutely crucial component of the modern data stack.

Data Warehouse 52

Data Warehouse Data Machine Learning Data Engineering 52

Grouparoo

MAY 21, 2020

How sure are you that Segment or Mixpanel is storing your customers' data safely? Google Analytics or Google Tag Manager? With regulations like GDPR and CCPA and many more on the way, you have a clear legal responsibility to keep your customers' data safe. Facebook or Twitter Pixels? The list goes on and on.

Data 52

Data Database Data Warehouse Building 52

Knowledge Hut

JUNE 20, 2023

Skills A data engineer should have good programming and analytical skills with big data knowledge. Examples Pull daily tweets from the data warehouse hive spreading in multiple clusters. Understanding how to construct data pipelines utilizing tools like Apache NiFi, Apache Kafka, and Apache Airflow is crucial.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

Knowledge Hut

NOVEMBER 16, 2023

This type of database management system uses sections of columns instead of rows to store the data. This is mainly used in big data, analytics, and data warehouse applications. Depending on the sort key you have specified for the table, the data will be spread across the cluster optimizing its ability to solve queries.

IT 52

IT Bytes AWS Data Warehouse 52

Cloudera

OCTOBER 18, 2022

However, we quickly found that our needs were more complex than the capabilities provided by the SaaS vendor and we decided to turn the power of CDP Data Warehouse onto solving our own cloud spend problem. This brings data directly into the Data Warehouse , which is stored as Parquet into Hive/Impala tables on HDFS.

Cloud 84

Cloud IT Data Warehouse AWS 84

Snowflake

NOVEMBER 13, 2023

After years of building up its modern data stack and establishing consensus around GDPR scope, Tide adopted and integrated Atlan to give it a 360-degree view of how data traveled across the organization. Using Snowflake as the organization’s data warehouse meant the team had a single repository for all its data.

Finance 79

Finance Consulting Data Warehouse Data Governance 79

Cloudera

NOVEMBER 25, 2020

How could Matthew serve all this data, together , in an easily consumable way, without losing focus on his core business: finding a cure for cancer. The Vision of a Discovery Data Warehouse. A Discovery Data Warehouse is cloud-agnostic. Access to valuable data should not be hindered by the technology.

Data Warehouse 57

Data Warehouse Unstructured Data Medical Pharmaceutical 57

Monte Carlo

APRIL 21, 2022

Adopting a cloud data warehouse like Snowflake is an important investment for any organization that wants to get the most value out of their data. Snowflake Data Quality Feature #3: Object Tagging Object Tagging in Snowflake. For example, tagging a column with phone numbers as PII = “Phone Number.”

Metadata 52

Metadata Bytes Government Data 52

Databand.ai

JULY 28, 2022

Lineage by data tagging: A transformation engine that tags every movement or change in data allows for lineage by data tagging. The system can then read those tags to visualize the data lineage. What is end-to-end lineage vs. data at rest lineage?

Metadata 52

Metadata Data Lake Datasets Data Warehouse 52

Data Engineering Podcast

JUNE 3, 2021

Firebolt is the fastest cloud data warehouse. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. The tag line calls out the fact that Querybook is an IDE for "big data" What are the manifestations of that focus in the feature set and user experience?

SQL 100

SQL Building Big Data BI 100

Knowledge Hut

MAY 2, 2024

These gigantic servers are stored in a data warehouse called a Datacenter. Below Diagram (2) indicates a single server serving and sharing resources and data among multiple client machines Does this look simplified enough? Latest tagged image: centOS_release1.2 That saves us time, resources, energy and revenue.

Python 52

Python Bytes Cloud Computing Amazon Web Services 52

Monte Carlo

FEBRUARY 26, 2024

Users can navigate through levels of data schema, run Google-like searches or use special crafted search operators to find what they are looking for. Popularity indicators help business users find the most in-demand information and governance tags can be used to structure data and define its access.

Metadata 52

Metadata Government Data Data Governance 52

DareData

AUGUST 12, 2021

These are your data ETL (extract, transform, load) processes, your CI/CD (continuous integration/continuous deployment) pipelines, and other automated processes involved in moving the data to the "refinery", which will be your data warehouses. Usually, these are tables containing a complete overview of your data.

Transportation 52

Transportation Data Lake Data Warehouse Data 52

ProjectPro

FEBRUARY 18, 2022

Why is ETL used in Data Science? It entails gathering data from numerous sources, converting it, and then storing it in a new single data warehouse. An ETL pipeline can help with the following tasks- Centralizes and standardizes data, making it more accessible to analysts and decision-makers.

Project 52

Project AWS Kafka Healthcare 52

Airbnb Tech

APRIL 13, 2021

Cost Explorer represented a significant improvement over the monthly invoice because it was possible to see data before the end of the month, but it did not provide detailed insights because it was not connected to Airbnb’s data tools. Most teams at Airbnb rely on the data warehouse (i.e.,

AWS 52

AWS Raw Data Amazon Web Services Cloud 52

Netflix Tech

DECEMBER 3, 2022

The most commonly used one is dataflow project , which helps folks in managing their data pipeline repositories through creation, testing, deployment and few other activities. It lets you create YAML formatted mock data files based on selected tables, columns and a few rows of data from the Netflix data warehouse.

Data Pipeline 143

Data Pipeline Scala Metadata Food 143

Monte Carlo

AUGUST 16, 2022

With Monte Carlo, data engineering teams can proactively manage the health of their data across the entire data stack — including data warehouses, data lakes, BI tools, data orchestration tools, and of course dbt — on a single platform.

Engineering 52

Engineering Data Lake Retail BI 52

Monte Carlo

FEBRUARY 8, 2024

How does it impact data warehouse/lakehouse performance? If you had an Airflow job that caused your data to fail, the real culprit wouldn’t be the data but a system issue. Or if a bad dbt model or data warehouse query change ultimately broke the data product downstream, that would be considered a code issue.

BI 59

BI Data Pipeline Data Warehouse Data 59

Snowflake

MARCH 30, 2023

A 2016 data science report from data enrichment platform CrowdFlower found that data scientists spend around 80% of their time in data preparation (collecting, cleaning, and organizing of data) before they can even begin to build machine learning (ML) models to deliver business value.

Engineering 79

Engineering Raw Data Data Science Scala 79