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. For metadata organization, they often use Hive, Amazon Glue, or Databricks. But, the options for data storage are evolving quickly.

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Data Lake Data Warehouse Unstructured Data Raw Data 52

Ripple Engineering

JANUARY 29, 2024

For Ripple's product capabilities, the Payments team of Ripple, for example, ingests millions of transactional records into databases and performs analytics to generate invoices, reports, and other related payment operations.   Ripple Data Consumers query the data from the lake storage using the SQL strategy.

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Database-centric Pipeline-centric NoSQL High Quality Data 52

Monte Carlo

JANUARY 16, 2024

Data lake and data warehouse convergence 2. Data lake and data warehouse convergence The data lake vs data warehouse question is constantly evolving. The maxim that data warehouses hold structured data while data lakes hold unstructured data is quickly breaking down. Table of Contents 1. Easier to stream real-time data 3.

Data Lake 52

Data Lake Data Warehouse Unstructured Data AWS 52

Towards Data Science

MARCH 6, 2023

On-premise and cloud working together to deliver a data product Photo by Toro Tseleng on Unsplash Developing a data pipeline is somewhat similar to playing with lego, you mentalize what needs to be achieved (the data requirements), choose the pieces (software, tools, platforms), and fit them together. And this is, by no means, a surprise.

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Google Cloud Cloud Storage Data Pipeline Cloud 84

Data Engineering Podcast

OCTOBER 16, 2022

Summary The "data lakehouse" architecture balances the scalability and flexibility of data lakes with the ease of use and transaction support of data warehouses. Dremio is one of the companies leading the development of products and services that support the open lakehouse. In fact, while only 3.5%

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Data Lake Food MongoDB Scala 100

Monte Carlo

JUNE 28, 2023

and Uniform The New Public Preview Features For Unity Catalog The Conclusion: Delta Sharing and Lakehouse Apps Keynote Kick-Off: Welcome To Generation AI Following a puff of smoke and red lazer light showm Databricks CEO, Ali Ghodsi, stormed the stage. We are going to lead the revolution.

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Data Warehouse Scala Unstructured Data Government 52

Monte Carlo

APRIL 24, 2023

Data lakes are useful, flexible data storage repositories that enable many types of data to be stored in its rawest state. 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.

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Data Lake Google Cloud Data Warehouse AWS 52

Scribd Technology

JULY 20, 2022

We also took advantage of the time to have multiple discussions with our colleagues at Databricks about their product and engineering roadmap, and where we can work together to improve the future of Delta Lake, Unity catalog, and more.

Kafka 52

Kafka Data Engineering IT 52

Ascend.io

JANUARY 23, 2023

Welcome to the first product update of 2023! Yet, since Ascend runs workloads in data cloud providers like Snowflake, Databricks, and BigQuery, the biggest costs are incurred in your accounts for these platforms. Check out Databricks documentation on this cool capability!

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Data Pipeline Data Ingestion Datasets Data 52

Data Engineering Weekly

NOVEMBER 26, 2023

We saw back-to-back articles comparing the features and the performance of Delta Lake, Apache Hudi, and Iceberg. Databricks is already doing a lot of these move-up stack products. Databricks announced a fleet of features for catalogs, governance, observability, and orchestration. The categories are merging.

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Data Engineering Data Engineer Engineering Kafka 70

Monte Carlo

JANUARY 5, 2024

Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake. Data lakehouse architecture is an increasingly popular choice for many businesses because it supports interoperability between data lake formats.

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Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake. Data lakehouse architecture is an increasingly popular choice for many businesses because it supports interoperability between data lake formats.

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Architecture Data Lake Metadata Unstructured Data 52

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. Cloud data lakes like Delta Lake have gotten so powerful (and popular) that according to Mordor Intelligence , the data lake market is expected to grow from $3.74

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Building Data Lake Business Intelligence Data Pipeline 52

Data Engineering Weekly

SEPTEMBER 3, 2023

link] Kyle Weller: Delta, Hudi, Iceberg — A Benchmark Compilation The LakeHouse architecture brings the best of the database and the data lake into the data infrastructure. Following a benchmark result between Delta & Iceberg , Apache Hudi added its benchmark in the same repo. or "Don't reinvent the wheel.".

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Data Engineering Data Engineer Engineering Data 73

Monte Carlo

JUNE 29, 2023

Check out what data leaders at both Snowflake Summit and Databricks Data + AI Summit 2023 had to say when it came to things like data adoption in the cloud, data trust, and of course, the now omnipresent Generative AI. However, that same survey revealed 73% intended to invest in a data warehouse or lake that same year.

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Data Warehouse Cloud Storage Cloud Machine Learning 52

Data Engineering Podcast

MAY 1, 2022

Open Source DataHub is running in production at several companies like Peloton, Optum, Udemy, Zynga and others. Acryl Data provides DataHub as an easy to consume SaaS product which has been adopted by several companies. What is the size of your team and how is it structured? What was the starting point on your platform journey?

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Towards Data Science

MARCH 9, 2023

The problem is when using the model in production it may lose code consistency among the team members and also the chances of human error in the code are higher when not operating a specially designed framework through the steps. It is perfectly possible to execute these steps using libraries like Pandas and NumPy or NLTK and SpaCy for NLP.

Machine Learning 77

Machine Learning Building Datasets Scala 77

Data Engineering Podcast

OCTOBER 9, 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. What have been the most challenging aspects of building a competitive business in such an active product category?

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Metadata AWS MongoDB MySQL 100

Towards Data Science

JULY 24, 2023

Of course, instead of life partners, engineers were starting to gleefully decouple storage and compute with emerging technologies like Snowflake (2012), Databricks (2013), and BigQuery (2010). While there is no right answer, there is likely a sweet spot for most organizations’ data platforms. Read on to see where that might be.

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Metadata Data Warehouse Data Lake Data Science 96

Knowledge Hut

NOVEMBER 2, 2023

Azure SQL Database, Azure Data Lake Storage). A real-world application for this data engineering project would be when a logistics company wishes to estimate the amounts of products that customers want delivered at various places in the future. This article contains the source code for the top 20 data engineering project ideas.

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Data Engineering Data Engineer Project Coding 52

Monte Carlo

AUGUST 28, 2023

Adam was a co-founder of Aerohive Networks and the open source AI project Donkey Carl, and — since 2018 — a product leader at Databricks. In fact, at Databricks, Adam suggests that partnering with Monte Carlo has given them the ability to build data observability across enterprises and dramatically improve reliability. “I

High Quality Data 69

High Quality Data Government Data Data Pipeline 69

Data Engineering Podcast

JULY 10, 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. By the time errors have made their way into production, it’s often too late and damage is done.

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Metadata MongoDB MySQL Data Pipeline 100

DataKitchen

JULY 27, 2023

Azure Databricks Workflows : An Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform. Workflows is a DAG runner embedded in Databricks. Databricks Notebooks are often used in conjunction with Workflows. Azure Blob Storage serves as the data lake to store raw data.

Data Pipeline 52

Data Pipeline BI Machine Learning Data Preparation 52

Scott Logic

APRIL 21, 2024

For instance, injecting Bloom filters and pushing them down the query plan, gave a claimed ~10x speedup on a TPC-DS benchmark for untuned Delta Lake sources and Parquet files. Apache Spark SQL has gone through a major evolution, now supporting ANSI SQL, and adding many new features and making many performance improvements.

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Scala Java SQL Python 52

Data Engineering Podcast

SEPTEMBER 4, 2022

Summary Data observability is a product category that has seen massive growth and adoption in recent years. 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.

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ProjectPro

FEBRUARY 21, 2023

Cloudera or Databricks? AWS or Azure? With so many data engineering certifications available , choosing the right one can be a daunting task. Businesses rely on the knowledge and skills of data engineers to deliver scalable solutions to their clients. The exam duration is 120 minutes, which includes a preparation time of 30 minutes.

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Certification Data Engineering Data Engineer Engineering 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. Interested in deploying data observability for your Delta Lake?

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Data Lake Machine Learning Data Science Data 52

Scribd Technology

JULY 19, 2021

Delta Lake is integral to our data platform which is why we have invested heavily in delta-rs to support our non-JVM Delta Lake needs. This year I had the opportunity to share the progress of delta-rs at Data and AI Summit. Suffice it to say, there’s a lot of really interesting work to be done!

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Python Kafka Data Engineering Data Engineer 40

Rockset

JANUARY 31, 2023

SkyHive platform Challenges with MongoDB for Analytical Queries 16 TB of raw text data from our web crawlers and other data feeds is dumped daily into our S3 data lake. Real-Time Data Stack with Databricks and Rockset We needed a storage layer capable of large-scale ML processing for terabytes of new data per day.

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MongoDB Pharmaceutical Recruitment Raw Data 59

Monte Carlo

OCTOBER 5, 2023

Databricks Clean Rooms On the other hand, Databricks Clean Rooms integrates big data and AI functionalities into a unified platform, dominating in analytics-intensive use cases. For example, with its Delta Lake storage layer, Apache Spark and big data workloads benefit from the reliability of ACID transactions.

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Raw Data Medical Manufacturing AWS 52

Scribd Technology

MAY 18, 2021

Streaming data from Apache Kafka into Delta Lake is an integral part of Scribd’s data platform, but has been challenging to manage and scale. We use Spark Structured Streaming jobs to read data from Kafka topics and write that data into Delta Lake tables. To serve this need, we created kafka-delta-ingest.

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Kafka Data Warehouse Bytes Metadata 52

Scott Logic

NOVEMBER 6, 2023

Data dimensions to be processed In transactional systems we are typically fetching or updating a row that is associated with a person or product but in analytics we often want to do aggregates across columns for all users or products within a time window/location/other. Regulatory reports need to be super accurate.

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Data Lake Kafka BI Unstructured Data 52

Striim

NOVEMBER 30, 2022

However, fragmented data can slow down the delivery of great product experiences and internal operations. However, fragmented data can slow down the delivery of great product experiences and internal operations. According to an IDG Market Pulse survey , companies collect data from 400 sources on average.

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AWS Cloud Management MySQL 52

Data Engineering Weekly

DECEMBER 21, 2022

We went through a full cycle that “schema-on-read ” led to the infamous GIGO (Garbage In, Garbage Out) problem in data lakes, as noted in this What Happened To Hadoop retrospect. Because the data warehouse is considered back office work, barely integrated with the product strategy. Why is it so?

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Data Engineering Data Engineer Engineering Hadoop 57

Scribd Technology

JULY 7, 2021

A big part of that story has been Databricks which we recently integrated with Terraform to make it easy to scale a top-notch developer experience. Just about anything they might need in Databricks is a pull request away! In hindsight, it’s mind-boggling how much manual configuration we had to previously maintain.

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Kafka Data Security Data Engineering Data Engineer 40

Monte Carlo

JULY 18, 2023

Of course, instead of life partners, engineers were starting to gleefully decouple storage and compute with emerging technologies like Snowflake (2012), Databricks (2013), and BigQuery (2010). This had amazing advantages from both a cost and scale perspective compared to on-premises databases.

Metadata 52

Metadata Data Warehouse Data Lake Government 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. In many cases the data stays in a data lake and is queried from there versus moving to the data warehouse. Zero ETL is a bit of a misnomer.

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Data Pipeline Architecture Data Lake Data Warehouse 52