Knowledge Hut

JANUARY 18, 2024

Data Science is an amalgamation of several disciplines, including computer science, statistics, and machine learning. In addition, top Data Science companies use complex algorithms and machine learning to solve problems or market their product better to consumers.

Data Science 98

Data Science Amazon Web Services Finance Big Data 98

Monte Carlo

NOVEMBER 28, 2023

This typically occurs when data teams want to move data from a transactional, on-premise, or staging database into the raw layer of their analytical data warehouse, lake or lakehouse. If you wanted to check the sum of sales by product, for example). 1% between source and target.” This can even be done by segment. (If

Database 59

Database Data Warehouse Machine Learning Data 59

Cloudera

JUNE 7, 2022

We are a Gartner Peer Insights Customer Choice for Cloud DBMS products. We deliver cloud-native data analytics across the full data lifecycle – data distribution, data engineering, data warehousing, transactional data, streaming data, data science, and machine learning – that’s portable across infrastructures.

IT 110

IT Unstructured Data Data Architecture Government 110

Monte Carlo

MARCH 21, 2024

This article was written in collaboration with Michael Segner, Product Marketing Manager at Monte Carlo and Bob Manfreda, Account Executive at Monte Carlo. It travels from transactional databases through real pipelines orchestrated with modern tooling before landing in the data warehouses and lakehouses most commonly used by data teams.

Data Warehouse 52

Data Warehouse Technology Database Machine Learning 52

Monte Carlo

APRIL 24, 2023

By accommodating various data types, reducing preprocessing overhead, and offering scalability, data lakes have become an essential component of modern data platforms , particularly those serving streaming or machine learning use cases. Databricks Data Catalog and AWS Lake Formation are examples in this vein.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Monte Carlo

MARCH 18, 2024

But, whether your data resides in a warehouse, lake, lakehouse, or on-prem, it’s important to take the time to assess the accuracy of your organization’s data. Some use cases here could be accommodating customers as part of a support/service motion or an ecommerce machine learning algorithm that recommends, “other products you might like.”

Datasets 52

Datasets Data Pipeline BI Data 52

Monte Carlo

SEPTEMBER 6, 2022

As part of this mission, Monte Carlo’s product, engineering, design, and data science teams are constantly releasing new product functionalities and features to improve the user experience and reduce time to detection, resolution, and prevention of broken data pipelines.

Data Lake 52

Data Lake Machine Learning Data Science Data 52

Monte Carlo

NOVEMBER 27, 2023

The answer today is the same as it was then: improve data product reliability by getting full context and visibility into your data systems. Perhaps the best way to think about it is to consider AI another data product and data observability as the living, breathing system that monitors ALL of your data products.

Unstructured Data 59

Unstructured Data Data Pipeline Data Banking 59

Monte Carlo

JANUARY 24, 2024

Modern data warehouses, data lakes, and data lakehouses have empowered multiple constituencies across companies to access and use data without always needing to tap the data engineering team for help. Thankfully, advances in AI and machine learning are making it possible to automate certain data governance tasks, like data observability.

Data Governance 52

Data Governance Government Data Lake Data Architecture 52

ProjectPro

DECEMBER 7, 2021

It can also consist of simple or advanced processes like ETL (Extract, Transform and Load) or handle training datasets in machine learning applications. Generally, data pipelines are created to store data in a data warehouse or data lake or provide information directly to the machine learning model development.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Monte Carlo

JULY 27, 2023

A financial dashboard may need to be accurate within a penny while a machine learning model may only need directionally accurate (but constantly refreshed) data. To account for this, data quality engineers will monitor data quality by designing and deploying data testing at scale (mentioned in 83% of job postings).

Engineering 52

Engineering Healthcare Scala Data Warehouse 52

Cloudera

OCTOBER 30, 2023

The public cloud is increasingly becoming the preferred platform to host data analytics – related projects, such as business intelligence, machine learning (ML), and AI applications. It’s not just a set of procedures or a piece of software to monitor cost and trigger alerts. Why FinOps?

Cloud 77

Cloud Finance Cloud Computing Government 77

Monte Carlo

AUGUST 8, 2023

Step 2: Build the pipeline Chances are you are not building a data pipeline entirely from scratch, but rather combining data from new sources with data already in your data warehouse or lakehouse. The reliability of your data product is as strong as its weakest link. The tests that monitor for drift or data anomalies.

Data Validation 52

Data Validation Data Pipeline SQL Data 52

Data Engineering Weekly

MARCH 12, 2023

Pedram Navid: dbt Reimagined The author writes about reimagining dbt and suggesting the possible area of improvement in dbt's core product. The advantage of DSL is that you can make more productivity improvements with compilers such as type safety, debuggers, etc. from the browser or mobile app). from the browser or mobile app).

Data Engineering 52

Data Engineering Data Engineer Engineering SQL 52

Monte Carlo

AUGUST 31, 2023

Your downstream data consumers including product analysts, marketing leaders, and sales teams rely on data-driven tools like CRMs, CXPs, CMSs, and any other acronym under the sun to do their jobs quickly and effectively. Data quality can be impacted at any stage of the data pipeline, before ingestion, in production, or even during analysis.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Data Engineering Weekly

FEBRUARY 26, 2023

Editor’s Note: Debunking Data Contracts; How Schemata Make Data Contracts Easy, Scalable, and Meaningful I talked about Schemata, Data Product Management, and Data Contracts in the last few weeks. Perhaps merge Data LakeHouse & Data Warehouse in the next edition? Riffing is a 5 step process that contains What is the goal?

Data Engineering 57

Data Engineering Data Engineer Engineering Raw Data 57

Databand.ai

DECEMBER 13, 2022

He has deep expertise in distributed systems, data engineering, API design, data integration from multiple sources, and machine learning. Follow Sarah on LinkedIn 5) Caleb Keller Principal Solutions Architect at Elastic Caleb is a mechanical engineer turned data scientist turned machine learning practitioner.

Data Engineering 52

Data Engineering Data Engineer Engineering AWS 52

Data Engineering Podcast

MAY 5, 2024

Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. What are the risks involved in deploying more human-level AI systems and monitoring their reliability? Closing Announcements Thank you for listening!

Building 147

Building Data Lake High Quality Data Machine Learning 147

Monte Carlo

NOVEMBER 9, 2023

I’d be hard-pressed to find a CTO who is willing to bet her career on buying or building a product that produces unreliable code or generates insights that are haphazard and misleading. Data observability gives teams the ability to monitor, alert, triage, and resolve data or pipeline issues at scale across your entire data ecosystem.

Unstructured Data 86

Unstructured Data Database Data Pipeline Architecture 86

Data Engineering Podcast

DECEMBER 24, 2023

Elad Eldor has experienced these challenges first-hand, leading to his work writing the book "Kafka: : Troubleshooting in Production" In this episode he highlights the sources of complexity that contribute to Kafka's operational difficulties, and some of the main ways to identify and mitigate potential sources of trouble.

Kafka 245

Kafka Data Lake High Quality Data SQL 245

Data Engineering Podcast

APRIL 14, 2024

Leverage Datafold's fast cross-database data diffing and Monitoring to test your replication pipelines automatically and continuously. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises.

Non-relational Database 147

Non-relational Database Relational Database Database Designing 147

Data Engineering Podcast

MARCH 31, 2024

While there are numerous products available to provide that visibility, they all have different technologies and workflows that they focus on. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises.

Project 130

Project Data Lake High Quality Data Data Workflow 130

Data Engineering Podcast

JANUARY 28, 2024

Summary Monitoring and auditing IT systems for security events requires the ability to quickly analyze massive volumes of unstructured log data. The majority of products that are available either require too much effort to structure the logs, or aren't fast enough for interactive use cases. e.g. cloudtrail logs, app logs, etc.

Data Lake 147

Data Lake Building High Quality Data AWS 147

ProjectPro

FEBRUARY 21, 2023

Expertise in creating scalable and efficient data processing architectures and also, monitor data processing systems. Can design, develop, and implement high-quality data processing systems and machine learning solutions. Can efficiently extract data and then transform and publish it for data-driven decision-making.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

AltexSoft

JUNE 26, 2023

They track people’s behavior on the Internet, initiate surveys, monitor feedback, listen to signals from smart devices, derive meaningful words from emails, and take other steps to amass facts and figures that will help them make business decisions. Data collection as the first step in the decision-making process, driven by machine learning.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Data Engineering Podcast

NOVEMBER 12, 2023

In this episode Eran Yahav shares the journey that he has taken in building this product and the ways that it enhances the ability of humans to get their work done, and when the humans have to adapt to the tool. It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products.

Software Engineer 147

Software Engineer Software Engineering Engineering Data Lake 147

Monte Carlo

SEPTEMBER 1, 2023

One of the stars taking center stage at the Databricks’ Data + AI Summit this year was their latest data quality feature, Lakehouse Monitoring. Now that Lakehouse Monitoring has entered public preview, more details have emerged that can help data teams understand its value and where it fits within their data reliability stack.

BI 52

BI Data Pipeline Data Data Lake 52

Data Engineering Podcast

DECEMBER 10, 2023

It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack Data projects are notoriously complex. What other tools/products might teams be evaluating while they consider Anomstack?

Data Lake 130

Data Lake High Quality Data SQL Architecture 130

Data Engineering Podcast

NOVEMBER 20, 2022

In this episode Ian Schweer shares his experiences at Riot Games supporting player-focused features such as machine learning models and recommeder systems that are deployed as part of the game binary. Just connect it to your database/data warehouse/data lakehouse/whatever you’re using and let them do the rest.

Systems 130

Systems Metadata Data Pipeline MongoDB 130

Monte Carlo

AUGUST 2, 2022

With Monte Carlo and Databricks’ partnership, teams can trust their data through end-to-end data observability across their lakehouse environments. Monte Carlo itself is built on the Databricks Lakehouse Platform, enabling our data and engineering teams to build and train our anomaly detection models at unprecedented speed and scale.

Building 52

Building Data Lake Business Intelligence Data Pipeline 52

Monte Carlo

JUNE 26, 2023

So, with that imperative in mind, it’s no surprise that we would continue our momentum with the Databricks team to bring even more observability to the Lakehouse. Read on to find out what has us so excited about the Lakehouse, everything we’ve announced so far, and what’s next for Databricks and Monte Carlo.

Data Lake 52

Data Lake Metadata Bytes Google Cloud 52

LinkedIn Engineering

JULY 19, 2023

Co-Authors: Sumedh Sakdeo , Lei Sun , Sushant Raikar , Stanislav Pak , and Abhishek Nath Introduction At LinkedIn, we build and operate an open source data lakehouse deployment to power Analytics and Machine Learning workloads. Figure 2 shows how OpenHouse fits into broader open source lakehouse deployments. GDPR purge).

Big Data 78

Big Data Data Management Management Metadata 78

Monte Carlo

FEBRUARY 8, 2024

This momentum shows no signs of stopping with data quality and reliability becoming a central topic in the data product and AI conversations taking place across organizations of all types and sizes. Data observability has been one of the hottest emerging data engineering technologies the last several years.

BI 59

BI Data Pipeline Data Warehouse Data 59

Monte Carlo

AUGUST 25, 2023

Different vendors offering data warehouses, data lakes, and now data lakehouses all offer their own distinct advantages and disadvantages for data teams to consider. Introducing data lakehouses Like “brunch” and “Bennifer”, data lakehouses are the portmanteau of the data warehouse amd data lake. Or maybe both.)

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Data Engineering Podcast

DECEMBER 25, 2022

With their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Once you have a potential tool, is the capabilty you want to use a unloved feature or a main part of the product.

Building 100

Building Metadata Business Intelligence Data Lake 100

Monte Carlo

MAY 23, 2023

As the company scales, various teams within Assurance—including data engineering, machine learning engineering, data science, business intelligence, and analytics engineering—leverage the platform to create new data assets. For example, the company can now scale more reliable machine learning using Monte Carlo monitors.

Insurance 52

Insurance Business Intelligence Machine Learning Data 52

Monte Carlo

JUNE 14, 2023

Some organizations choose to still leverage an ETL pattern in the cloud, particularly for production pipelines where data contracts can help reduce data downtime. Streaming data pipeline architectures are typically run in parallel to modern data stack pipelines and used mainly for data science or machine learning use cases.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52