Monte Carlo

JANUARY 29, 2024

Snowflake object tagging enables data teams to easily organize and classify their data objects with helpful context like ownership and priority level. And with Monte Carlo’s deep Snowflake integration, those object tags can also be automatically imported and applied to your Monte Carlo Access View as well. The other half?

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Lyft Engineering

OCTOBER 3, 2023

In this post we will define data quality at a high-level and explore our motivation to achieve better data quality. We will then introduce our in-house product, Verity, and showcase how it serves as a central platform for ensuring data quality in our Hive Data Warehouse. What and Where is Data Quality?

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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

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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. Data warehouse vs. data lake in a nutshell.

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Cloudera

NOVEMBER 12, 2020

Your sunk costs are minimal and if a workload or project you are supporting becomes irrelevant, you can quickly spin down your cloud data warehouses and not be “stuck” with unused infrastructure. Cloud deployments for suitable workloads gives you the agility to keep pace with rapidly changing business and data needs.

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Christophe Blefari

SEPTEMBER 28, 2023

When you put the things like this it opens the doors and does not limit the modern data stack to 4 vendors. The central storage can be cloud storage, a warehouse, a real-time system, while the SQL engine can be a data warehouse or a dedicated processing engine. For this you need to do recurrent data cleaning.

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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.

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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.”

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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?

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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?

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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.

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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.

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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

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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?

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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.

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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.

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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.,

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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?

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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.

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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.

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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!

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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.

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AltexSoft

NOVEMBER 23, 2021

Before we get into more detail, let’s determine how data virtualization is different from another, more common data integration technique — data consolidation. Data virtualization vs data consolidation. The example of a typical two-tier architecture with a data lake and data warehouses and several ETL processes.

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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.

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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.

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Monte Carlo

JUNE 9, 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. Most data teams, especially those early in their Snowflake journey, have yet to fully unlock full potential and value from this key investment. as well as reliability.

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Cloudera

SEPTEMBER 9, 2022

analyst Sumit Pal, in “Exploring Lakehouse Architecture and Use Cases,” published January 11, 2022: “Data lakehouses integrate and unify the capabilities of data warehouses and data lakes, aiming to support AI, BI, ML, and data engineering on a single platform.” According to Gartner, Inc.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Monte Carlo

NOVEMBER 1, 2022

Our partners in the finance department can do remarkable things with a PC and a VLOOKUP, and in my experience there are few better ways to democratize the prototyping of new data than by dropping a table into Google Sheets and collaborating with partners to manually append tags that are meaningful attributes for them to see in analysis.

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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.,

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DoorDash Engineering

APRIL 12, 2023

Challenges of ad-hoc SQLs Our initial goal with Curie was to standardize the analysis methodologies and simplify the experiment analysis process for data scientists. Core Data Models / Semantics We placed a strong emphasis on identifying the most comprehensive and effective core data models for users to create their own metrics.

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