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

AUGUST 2, 2022

As companies increasingly leverage data-driven insights to innovate and maintain their competitive edge, it’s essential that this data is accurate and reliable. With Monte Carlo and Databricks’ partnership, teams can trust their data through end-to-end data observability across their lakehouse environments. billion in 2020 to $17.60

Building 52

Building Data Lake Business Intelligence Data Pipeline 52

Monte Carlo

JANUARY 12, 2024

But what is new, or still developing at least, is the extent to which data engineers can manage, data scientists can experiment, and data analysts can analyze this treasure trove of raw business insights. Data observability platforms like Monte Carlo alert data teams to schema, volume, freshness, and distribution anomalies.

Big Data 59

Big Data Data Analytics Data Science Data Lake 59

Monte Carlo

MAY 2, 2023

Respondents averaged 642 tables across their data lake, lakehouse, or warehouse environments. The number one reason for launching a data quality initiative was that the data organization identified data quality as a need ( 28% ), followed by a migration or modernization of the data platform or systems ( 23% ).

Data Lake 80

Data Lake Data Software Engineer Software Engineering 80

Cloudera

JUNE 24, 2022

Navigating growing geopolitical tensions and conflicts: Agencies operate in a time of immense complexity, from rising inflation to war in Ukraine to a vast cybersecurity threat landscape (and the recent Executive Order on Improving the Nation’s Cybersecurity ). They cannot be asked to solve tomorrow’s problems with yesterday’s technology.

Government 76

Government Metadata Data Algorithm 76

Cloudera

AUGUST 28, 2023

In the following section, we are going to walk you through our newest Applied Machine Learning Prototype (AMP), “LLM Chatbot Augmented with Enterprise Data”. For those of us in the data world, Iceberg more often than not refers to an open source high-performance table format that’s the foundation of the Open Lakehouse. No worries!

Machine Learning 76

Machine Learning Data Accessible Accessibility 76

Cloudera

APRIL 3, 2023

Cloudera Contributors: Ayush Saxena, Tamas Mate, Simhadri Govindappa Since we announced the general availability of Apache Iceberg in Cloudera Data Platform (CDP), we are excited to see customers testing their analytic workloads on Iceberg. This library is integrated by execution engines such as Impala, Hive and Spark.

Data Warehouse 81

Data Warehouse Metadata Java Data 81

Cloudera

JANUARY 5, 2023

Faced with even more pressure to remain resilient and agile amid looming global economic threats, Asia-Pacific (APAC) region businesses are looking to further mobilize emerging technologies such as artificial intelligence (AI) and machine learning that will optimize operational efficiencies and cost savings. .

Banking 52

Banking Machine Learning Insurance Data Architecture 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. Cloudera FinOps Capabilities CDP is a cloud-native platform helping companies accelerate cloud adoption to run their data and analytics workloads.

Cloud 77

Cloud Finance Cloud Computing Government 77

ProjectPro

DECEMBER 7, 2021

Data Pipeline Tools AWS Data Pipeline Azure Data Pipeline Airflow Data Pipeline Learn to Create a Data Pipeline FAQs on Data Pipeline What is a Data Pipeline? It can also consist of simple or advanced processes like ETL (Extract, Transform and Load) or handle training datasets in machine learning applications.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Monte Carlo

JUNE 28, 2022

To help organizations realize the full potential of their data lake and lakehouse investments, Monte Carlo, the data observability leader, is proud to announce integrations with Delta Lake and Databricks’ Unity Catalog for full data observability coverage.

Data Lake 52

Data Lake Metadata AWS Data Warehouse 52

Monte Carlo

JULY 27, 2023

Like much of the B2B SaaS universe, costs are based on platform usage. These data optimization initiatives need to be executed with an understanding of the overall business strategy. But perhaps the best way to understand the business context of different data platform costs is to talk to the business.

Data 52

Data Architecture Data Warehouse Data Engineering 52

Monte Carlo

AUGUST 31, 2023

These conversations inspired us to write this book to convey some of the best practices we’ve learned and developed related to managing data quality at each stage of the data pipeline, from ingestion to analytics, and share how data teams in similar situations may be able to prevent their own data downtime.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Monte Carlo

FEBRUARY 8, 2024

The core features of data observability tools were originally defined across five pillars that included four types of machine learning anomaly detection monitors and data lineage : Freshness – Did the data arrive when it was expected? How does it impact data warehouse/lakehouse performance? Robust role based access controls?

BI 59

BI Data Pipeline Data Warehouse Data 59

Rockset

JANUARY 31, 2023

SkyHive is an end-to-end reskilling platform that automates skills assessment, identifies future talent needs, and fills skill gaps through targeted learning recommendations and job opportunities. And we have done a lot of complex analytics and machine learning to glean insights into global job trends today and tomorrow.

MongoDB 59

MongoDB Pharmaceutical Raw Data Recruitment 59

Monte Carlo

NOVEMBER 9, 2023

And it’s not their fault – executives and boards are to blame for much of the “hurry up and go” mentality around this (and most) new technologies. 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.

Unstructured Data 86

Unstructured Data Database Data Pipeline Architecture 86

AltexSoft

MARCH 14, 2023

Having gained insights from the data, the travel company can offer personalized recommendations, improve customer satisfaction, and ultimately drive business growth. Built around a cloud data warehouse, data lake, or data lakehouse. But as we all know, the word “modern” never comes out of nowhere. Designed to be modular.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Monte Carlo

MAY 25, 2023

That’s why we’re excited to announce that uniting diverse data team personas to collectively ensure data quality just got easier, thanks to a new integration between Monte Carlo’s data observability platform and Atlan’s active metadata platform.

Metadata 52

Metadata Data Governance Government Business Intelligence 52

Towards Data Science

JULY 24, 2023

While there is no right answer, there is likely a sweet spot for most organizations’ data platforms. A decade later, a considerable amount of innovation in the data management industry is revolving around how different data platforms are coupling or decoupling storage and compute (don’t worry I include examples in the next section).

Metadata 96

Metadata Data Warehouse Data Lake Data Science 96

Monte Carlo

AUGUST 9, 2023

Simply put, a data engineering workflow is a series of operations followed in sequence by data engineering teams to scalably, repeatedly, and reliably execute DataOps tasks. Building data platforms, pipelines, and products would become incredibly slow and inefficient. Data engineers work with data, or files that code operates on.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Pipeline 52

Monte Carlo

AUGUST 15, 2023

PLATFORMS THAT SEPARATE STORAGE AND COMPUTE (BUT OFFER BOTH SERVICES) ARE AMONG THE MOST POPULAR FOR THE ADVANTAGES THEY PROVIDE. They are: Moving from ETL to ELT to accelerate time-to-insight You can’t just load anything into your on-premise database– especially not if you want a query to return before you hit the weekend.

Data Storage 88

Data Storage Cloud Metadata Media 88

Monte Carlo

JUNE 14, 2023

The modern data stack era , roughly 2017 to present data, saw the widespread adoption of cloud computing and modern data repositories that decoupled storage from compute such as data warehouses, data lakes, and data lakehouses. Stitch – A cloud-based open source platform that allows you to rapidly move data from a source to a destination.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Towards Data Science

AUGUST 15, 2023

They are: Moving from ETL to ELT to accelerate time-to-insight You can’t just load anything into your on-premise database– especially not if you want a query to return before you hit the weekend. We want to empower AutoTrader and its customers to make data-informed decisions and democratize access to data through a self-serve platform….As

Cloud 83

Cloud Metadata Media Machine Learning 83

AltexSoft

JULY 18, 2023

This article will expose Apache Spark architecture, assess its advantages and disadvantages, compare it with other big data technologies, and provide you with the path to learning this impactful instrument. What is Apache Spark? Apache Spark components. Netflix leverages Spark Streaming and Kafka for near real-time movie recommendations.

Big Data 52

Big Data Data Process Process Hadoop 52

Ascend.io

AUGUST 31, 2023

Modern Cloud Data Platforms The native capabilities of the cloud providers have been joined by third-party services to offload that data into separate less costly systems that are optimized for analysis of that data. Modern platforms like Redshift , Snowflake , and BigQuery have elevated the data warehouse model.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Monte Carlo

AUGUST 15, 2023

Platforms that separate storage and compute (but offer both services) are among the most popular for the advantages they provide. They are: Moving from ETL to ELT to accelerate time-to-insight You can’t just load anything into your on-premise database– especially not if you want a query to return before you hit the weekend.

Cloud 69

Cloud Metadata Media Machine Learning 69

Databand.ai

DECEMBER 13, 2022

Whether you’re a data engineering pro looking to stay up to date on the latest trends or new to the space and want to learn more, following the right leaders and joining the right conversations can make all the difference when it comes to plugging into the data engineering community. You’ve come to the right place.

Data Engineering 52

Data Engineering Data Engineer Engineering AWS 52

Monte Carlo

JULY 18, 2023

A decade later, a considerable amount of innovation in the data management industry is revolving around how different data platforms are coupling or decoupling storage and compute (don’t worry I include examples in the next section). I believe–at this current moment–that range for data platforms follows Aristotle’s golden mean.

Metadata 52

Metadata Data Warehouse Data Lake Government 52

Cloudera

JUNE 16, 2022

real-time data streams) and generate immediate insights for faster decision making provides a competitive edge for organizations. . CSP was recently recognized as a leader in the 2022 GigaOm Radar for Streaming Data Platforms report. The ability to perform analytics on data as it is created and collected (a.k.a.

Kafka 86

Kafka Manufacturing Data Lake SQL 86

Monte Carlo

AUGUST 8, 2023

Like most things, there will be diminishing returns for each new test and new challenges that arise from attempting to execute it at scale. 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.

Data Validation 52

Data Validation Data Pipeline SQL Data 52

Ascend.io

APRIL 7, 2023

With it, they could discover invaluable insights, pivot in real-time, and connect their work to direct revenue impact. Data mesh is a platform architecture that decentralizes and democratizes an organization’s data by organizing it into clearly defined domains. But they’re living in what is essentially the opposite of their fantasy.

Metadata 52

Metadata Datasets Data Governance Government 52

Data Science Blog: Data Engineering

NOVEMBER 15, 2023

The trend towards powerful in-house cloud platforms for data and analysis ensures that large volumes of data can increasingly be stored and used flexibly. For example, when creating an order in the ERP system, the opening and closing of an input screen could be recorded as a timestamp and the execution time of the micro-task analyzed.

Architecture 52

Architecture Database-centric Process BI 52

Monte Carlo

MARCH 28, 2024

A data product typically hides the complexity of the data pipelines from the data consumers downstream, so they’re not exposed to or overwhelmed by the data movement, virtualization, caching, lakehouse, fabric, or other components involved in data asset creation. The following quotes were edited for length and clarity.)

Data 52

Data High Quality Data Machine Learning Accessible 52

Monte Carlo

JULY 18, 2023

A decade later, a considerable amount of innovation in the data management industry is revolving around how different data platforms are coupling or decoupling storage and compute (don’t worry I include examples in the next section). I believe–at this current moment–that range for data platforms follows Aristotle’s golden mean.

Metadata 40

Metadata Data Warehouse Data Lake Government 40

Monte Carlo

MAY 17, 2023

Keep Critical Machine Learning Algorithms Online 27. Data Warehouse (Or Lakehouse) Migration 34. Execute A Smooth Data Catalog Roll Out Go deeper and broader with your data quality coverage 59. Save Analytical Engineer’s Time Increase revenue 22. Improve Marketing Campaigns 23. Enable A Robust Experimentation Infrastructure 24.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

Keep Critical Machine Learning Algorithms Online 27. Data warehouse (or Lakehouse) migration 34. Execute A Smooth Data Catalog Roll Out Go deeper and broader with your data quality coverage 59. Save analytical engineer’s time Increase revenue 22. Improve Marketing Campaigns 23. Enable A Robust Experimentation Infrastructure 24.

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52

AltexSoft

JULY 19, 2022

In the last few decades, we’ve seen a lot of architectural approaches to building data pipelines , changing one another and promising better and easier ways of deriving insights from information. For example, your organization has an HR platform that produces employee data. And whenever we started thinking, “Hey, that’s it.

Architecture 52

Architecture Data Lake Medical Datasets 52