Ascend.io

JANUARY 23, 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!

Data Pipeline 52

Data Pipeline Data Ingestion Datasets Data 52

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. But it’s not so easy… Why is enterprise AI hard to achieve? Why reinvent the wheel when you have the foundations in place? Remember NFTs?). No easy feat.

Unstructured Data 86

Unstructured Data Database Data Pipeline Architecture 86

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. billion in 2020 to 17.60

Data Lake 52

Data Lake Metadata AWS Data Warehouse 52

Pipeline Data Engineering

NOVEMBER 4, 2021

Why Lightspeed invested in ClickHouse: a database built for speed Gaurav Gupta, VC, Lightspeed Venture Partners $250M Series B financing of ClickHouse. News and interesting reads about the world of data. Eating the Cloud from Outside In Shawn Wang, Developer Experience, Temporal.io AWS is playing Chess. Cloudflare is playing Go.

PostgreSQL 52

PostgreSQL Finance Consulting Software Engineer 52

ProjectPro

FEBRUARY 21, 2023

Cloudera or Databricks? Why Are Data Engineering Skills In Demand? Proficient in building data processing solutions using Azure Data Factory , Azure Synapse Analytics , Azure Data Lake Storage, Azure Databricks , etc. AWS or Azure? Don’t worry! This exam can be taken only in the English language.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

DataKitchen

JUNE 12, 2023

Why the Data Journey Manifesto? So why another manifesto in the world? Why should I care? The post Why the Data Journey Manifesto? About seven years ago, we wrote the DataOps Manifesto. Those tools work together to take data from its source and deliver it to your customers. It’s 22 principles on one static web page.

ETL Tools 52

ETL Tools Data Science Data Lake Hadoop 52

Ascend.io

JUNE 21, 2023

This conundrum is why data product costing is one of the core capabilities of the Ops Plane in the Ascend platform, specifically designed to address the business-critical need of controlling costs and understanding ROI. Sample anticipated Databricks billing breakdown to identify sources of costs in each cluster.

Pipeline-centric 52

Pipeline-centric Data Pipeline Metadata Data 52

Knowledge Hut

NOVEMBER 17, 2023

Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure Storage, Azure Data Lake, Azure Blob Storage, Azure Cosmos DB, Azure Stream Analytics, Azure HDInsight, and other Azure data services are just a few of the many Azure data services that Azure data engineers deal with. Why Choose Azure Data Engineer?

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

dbt Developer Hub

MAY 10, 2022

There’s always a little lack of rhyme or reason for why some values are passed as upper or lowercase, and it’s not worth the headache to unpack that. Data warehouse support for the LOWER function ​ Google BigQuery , Amazon Redshift , Snowflake , Postgres , and Databricks all support the LOWER function.

SQL 40

SQL IT BI Data Warehouse 40

dbt Developer Hub

JULY 25, 2022

we introduced a grants config that works a lot like post-hook , with two key differences: You configure grants as a structured dictionary rather than writing all the SQL yourself dbt will take the most efficient path to apply those grants Why grants are better than hooks ​ First of all, hooks are hard ! values ( ) % } { % set grants = node.

BI 52

BI Metadata Database SQL 52

Ascend.io

AUGUST 31, 2023

By marrying the tried-and-true practices of traditional warehousing with modern scalability, these platforms eliminate the cumbersome tasks associated with on-prem management while enabling the execution of analysis, reporting, and ad-hoc inquiries.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Advancing Analytics: Data Engineering

SEPTEMBER 7, 2021

These dimensions are also affected by the passage of time and require revised descriptions periodically which is why they are known as Slowly Changing Dimensions (SCD). See The Data Warehouse Toolkit - Kimball & Ross for more information. Here is where the Delta Lake comes in. SQL MERGE INTO scdType1 USING scdType1NEW ON scdType1.employee_id

Data Lake 52

Data Lake Datasets Data Warehouse SQL 52

Towards Data Science

APRIL 17, 2023

What should be an opportunity has become a crisis as organizations discover they aren’t close enough to the business to explain why they ordered the lobster. Executives and decision makers looked at who was winning–the Googles, Netflixes, and Amazons of the world–and investing in data seemed like a no brainer. How did we get here?

Data 97

Data Data Engineering Data Engineer Engineering 97

phData: Data Engineering

JULY 12, 2022

We’ll also go through the benefits of utilizing dbt for data modeling and ultimately why it’s a great tool for data modeling. Data engineers can also leverage dbt for managing and executing data model transformations in their existing pipelines with few trade-offs and some skill-up in the business context of the data.

SQL 52

SQL Data Engineering Data Engineer Data 52

Data Engineering Weekly

APRIL 16, 2023

However, as the data stack consolidation accelerates, we will see a much better permission model from Snowflake and Databricks. #3 While reading Airbnb’s Minerva blog about XRF[ eXecutive Reporting Framework] that aggregate, I can’t stop wondering how expensive this system will be. 2 I agree; permission is a mess.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Knowledge Hut

OCTOBER 29, 2023

Why Microsoft Azure is Important Today? An Azure Databricks service to preprocess and transform the data and run the workloads. You can create a workflow that can pacify the entire process end-to-end and execute tasks at call centers even without user actions. An Azure Machine Learning instance for the AI capabilities.

Project 52

Project Hospitality Food Datasets 52

Knowledge Hut

SEPTEMBER 28, 2023

Why Should You Get Azure Certification? Executives in charge of an IT team will discover that earning an Azure certification can be a great way to assess the skills of more experienced team members in addition to developing a solid understanding of Azure services and solutions.

Certification 52

Certification Cloud Computing Recruitment Cloud 52

Knowledge Hut

SEPTEMBER 25, 2023

Here are the reasons why: 1. Design data processing solutions using Azure Data Factory and Azure Databricks. Proficiency in these languages facilitates effective communication with senior executives by translating technical intricacies into tangible business value.

Certification 52

Certification Cloud Computing Programming Language Cloud 52

Maxime Beauchemin

JANUARY 20, 2017

In smaller environments people tend to use hosted services offered by Amazon or Databricks, or get support from companies like Cloudera or Hortonworks — which essentially subcontracts the data engineering role to other companies. This includes tasks like setting up and operating platforms like Hadoop/Hive/HBase, Spark, and the like.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

ProjectPro

JANUARY 19, 2022

Why do companies hire a Data Engineer? Why do companies hire a Data Engineer? And that is why the business managers look for data engineers, as they are the ones who will interact with the raw data, clean it, polish it, and make it analysis-ready. And, that is why most beginners are instantly inclined towards exploring AWS.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Databand.ai

DECEMBER 13, 2022

Caleb also offers executive coaching, IT consulting, project management, and more, and his LinkedIn page focuses on sharing advice and experiences around data reporting, strategic planning, data analytics, machine learning, and data engineering. You can find her frequently sharing her knowledge and expertise on her Medium blog.

Data Engineering 52

Data Engineering Data Engineer Engineering AWS 52

Monte Carlo

AUGUST 31, 2023

Next, we’ll frame the current moment to better understand why data quality is more important for data leaders than ever before. It’s important to note that the concept of data mesh is nascent, and there is much discussion in the data community regarding how (or whether it makes sense) to execute on one at both a cultural and technical level.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Knowledge Hut

MAY 3, 2024

It provides one execution model for all tasks and hence very easy for developers to learn and they can work with multiple APIs easily. The core is the distributed execution engine and the Java, Scala, and Python APIs offer a platform for distributed ETL application development. Why Apache Spark? Yarn etc) Or, 2.

Scala 98

Scala Hadoop Healthcare Big Data 98

Monte Carlo

JUNE 29, 2021

Why is it so hard to hire data engineers? According to Javier Pelayo , Director of Platform Engineering, Big Data, at Adidas , setting benchmarks with your executive leadership entails not just strong vision but also a way to tie the impact of the data platform and other data products to the company’s larger technology and business goals.

Data Engineering 40

Data Engineering Data Engineer Engineering Recruitment 40

Monte Carlo

MAY 31, 2022

To retrieve data using SQL, a user would write and execute a query, which is then typically stored in a log. Following are examples from Databricks notebooks in Python, Scala, and R that all do the same thing – load a CSV file into a Spark DataFrame. That makes the DAG the equivalent of a SQL execution plan.

Data Lake 52

Data Lake Building Scala Metadata 52

Towards Data Science

MARCH 9, 2023

It is perfectly possible to execute these steps using libraries like Pandas and NumPy or NLTK and SpaCy for NLP. This is why ML teams tend to work with high-level APIs called Pipelines, specially designed to link all the stages in a single object. Image 2— Starting the Databricks cluster. Source: The author. Source: The author.

Machine Learning 78

Machine Learning Building Datasets Scala 78

Knowledge Hut

MAY 2, 2024

Why We Need Big Data Frameworks Big data is primarily defined by the volume of a data set. Market Demands for Spark and MapReduce Apache Spark was originally developed in 2009 at UC Berkeley by the team who later founded Databricks. Spark achieves this using DAG, query optimizer, and a highly optimized physical execution engine.

Scala 94

Scala Hadoop Datasets Java 94

ProjectPro

NOVEMBER 14, 2015

Table of Contents Why Apache Hadoop? Why Apache Spark? Why Apache Spark? Hadoop ecosystem has a very desirable ability to blend with popular programming and scripting platforms such as SQL, Java , Python, and the like which makes migration projects easier to execute. Data Migration 2. Data Integration 3.Scalability

Hadoop 52

Hadoop Project Big Data Healthcare 52

Data Engineering Podcast

JULY 16, 2021

Raj Bains founded Prophecy to address this need by creating a UI first platform for building and executing data engineering workflows that orchestrates Airflow and Spark. Why is data engineering still such a complicated and challenging undertaking? What would be involved in adopting other execution and orchestration engines?

High Quality Data 100

High Quality Data Data Engineering Data Engineer Coding 100

Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? Why is data pipeline architecture important? No copy data sharing is another emerging data pipeline architecture being pioneered by Snowflake ( Secure Data Sharing ) and Databricks ( Delta Sharing ). Remember, these aren’t a binary choice.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

ProjectPro

FEBRUARY 18, 2022

Let us now understand why the ETL pipelines hold such great value in Data Science and Analytics. Table of Contents Why is ETL used in Data Science? Why is ETL used in Data Science? You'll also have the opportunity to know more about Azure Databricks, Data Factory , and Storage services.

Project 52

Project AWS Kafka Healthcare 52

Rockset

OCTOBER 26, 2023

That’s why JetBlue innovates with real-time analytics and AI, using over 15 machine learning applications in production today for dynamic pricing, customer personalization, alerting applications, chatbots and more. That’s why chat experiences like “how long is my flight delayed by” need to generate responses in under a second.

Machine Learning 64

Machine Learning Data Science Architecture Data Warehouse 64

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). Why are these things related, and more importantly, why should data leaders care?

Metadata 97

Metadata Data Warehouse Data Lake Data Science 97

LinkedIn Engineering

JULY 27, 2023

That’s why we built Costwiz, a tool that allows us to reduce costs by helping teams keep an eye on budgets and over-provisioned or under-utilized resources. Data providers: The core input data for which the workflows should be executed and other supporting data required are fed to the system by data providers.

Metadata 75

Metadata Utilities Cloud Data Lake 75

Knowledge Hut

MARCH 14, 2024

Cloud computing, which enables you to execute any software on any computer in a data center, is based on the virtualization principle. It's the reason why apps and websites can handle lots of users without crashing and why they work fast all around the world.

Architecture 52

Architecture Cloud Computing Utilities Machine Learning 52

ProjectPro

JANUARY 31, 2022

Databricks Snowflake Projects for Practice in 2022 Dive Deeper Into The Snowflake Architecture FAQs on Snowflake Architecture Snowflake Overview and Architecture With Data Explosion, acquiring, processing, and storing large or complicated datasets appears more challenging. This is the reason why we need Data Warehouses.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

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. And nearly 75% of the time, business stakeholders are the first to identify data issues.

Metadata 52

Metadata Data Governance Government Business Intelligence 52