Data Engineering Podcast

JULY 16, 2021

Summary There is a wealth of tools and systems available for processing data, but the user experience of integrating them and building workflows is still lacking. 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.

High Quality Data 100

High Quality Data Data Engineering Data Engineer Coding 100

Workfall

JULY 4, 2023

With DBT, they weave powerful SQL spells to create data models that capture the essence of their organization’s information. DBT’s superpowers include seamlessly connecting with databases and data warehouses, performing amazing transformations, and effortlessly managing dependencies to ensure high-quality data.

Data Warehouse 69

Data Warehouse SQL PostgreSQL Database 69

Data Engineering Podcast

JULY 22, 2021

In this episode Nick Handel shares the story behind Transform, a new platform that provides a managed metrics layer for your data platform. Now there’s a book that captures the foundational lessons and principles that underly everything that you hear about here. No more scripts, just SQL. No more scripts, just SQL.

SQL 100

SQL BI Data Warehouse Data Engineering 100

Cloudera

DECEMBER 12, 2018

I was a student system administrator for the campus computing group and at that time they were migrating the campus phone book to a new tool, new to me, known as Oracle. After having rebuilt their data warehouse, I decided to take a little bit more of a pointed role, and I joined Oracle as a database performance engineer.

Data Warehouse 42

Data Warehouse Relational Database Hadoop BI 42

Data Engineering Podcast

JULY 9, 2021

Summary Every data project, whether it’s analytics, machine learning, or AI, starts with the work of data cleaning. This is a critical step and benefits from being accessible to the domain experts. Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads?

SQL 100

SQL BI Data Engineering Data Engineer 100

Monte Carlo

JANUARY 23, 2024

Data engineering was a thankless but increasingly important job, with data engineering teams straddling between building infrastructure, running jobs, and fielding ad-hoc requests from the analytics and BI teams. As a result, being a data engineer was both a blessing and a curse. Data debt or data pipeline chaos.

Data Engineering 98

Data Engineering Data Engineer Engineering Data 98

AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Data Engineering Podcast

NOVEMBER 9, 2020

He also shares the internal architecture, how he approached the design to make it accessible and easy to use, and how it autodiscovers the schemas and metadata for your source systems. If you hand a book to a new data engineer, what wisdom would you add to it?

Building 100

Building PostgreSQL BI Metadata 100

AltexSoft

JUNE 26, 2023

Commonly, the entire flow is fully automated and consists of three main steps — data extraction, transformation, and loading ( ETL or ELT , for short, depending on the order of the operations.) Dive deeper into the subject by reading our article Data Integration: Approaches, Techniques, Tools, and Best Practices for Implementation.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Ascend.io

FEBRUARY 15, 2024

Despite the availability of tools that offer a plug-and-play experience, the lack of a deep understanding of these strategies can lead to inefficient data management, potentially derailing your plans to create slowly changing dimensions in your data warehouse. Why are Data Replication Strategies Important?

Datasets 52

Datasets Database Data Warehouse Data 52

Monte Carlo

APRIL 20, 2023

Solutions with automated data lineage capabilities constantly update these graphs and illustrate them as nodes and edges, or in other words, the objects through which the data travels and the relationship between them. This is one of the most frequent data lineage use cases leveraged by Vox.

Data Warehouse 52

Data Warehouse BI Data Government 52

Data Engineering Podcast

NOVEMBER 30, 2020

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it?

Data Integration 100

Data Integration Coding BI Kafka 100

Data Engineering Podcast

NOVEMBER 23, 2020

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it?

Hadoop 100

Hadoop Data Pipeline BI Data 100

dbt Developer Hub

JULY 2, 2023

Data Vault 2.0 is a data modeling technique designed to help scale large data warehousing projects. It is a rigid, prescriptive system detailed vigorously in a book that has become the bible for this technique. So why Data Vault? This promotes a scalable data warehouse through reusability and modularity.

Cloud 52

Cloud Data Warehouse Data BI 52

Monte Carlo

NOVEMBER 10, 2021

Monte Carlo’s field-level lineage helps data teams track column-level dependencies from ingestion in the data warehouse or lake to dashboards and reports in the BI layer. Access to field-level lineage allows our customers to reduce the Time to Resolution when data incidents occur using Incident IQ.

Data Warehouse 52

Data Warehouse BI Business Intelligence SQL 52

Knowledge Hut

SEPTEMBER 26, 2023

When designing, constructing, maintaining, and troubleshooting data pipelines that transfer data from its source to the proper storage place and make it accessible for analysis and reporting, we collaborate with data architects and data scientists. ETL activities are also the responsibility of data engineers.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

ProjectPro

JANUARY 19, 2022

The first step is to work on cleaning it and eliminating the unwanted information in the dataset so that data analysts and data scientists can use it for analysis. That needs to be done because raw data is painful to read and work with. How much python should you know to become a data engineer?

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Databand.ai

DECEMBER 29, 2022

Here’s the criteria we looked at when creating this list (not all need to apply): # of LinkedIn followers Passion for data strategy across disciplines (e.g. Quality, Engineering, Security) Frequent keynote speaker, book author, community leader (e.g. Mico actively posts about data analytics, BI strategy, and data storytelling.

BI 52

BI Consulting Data Science Data Governance 52

Knowledge Hut

JANUARY 22, 2024

To put this in perspective, it is estimated that the entire contents of books printed ever hold 10 terabytes of data in all printed materials. So, one exabyte could hold 100,000 times the printed material, or 500 to 3,000 times the entire contents of the books printed, and we produce 3.3 exabytes of data every month!

Data Science 52

Data Science Banking Big Data Insurance 52

Knowledge Hut

SEPTEMBER 6, 2023

Database Management : knowing how to work with databases - both relational(like Postgres) and non-relational - is important for efficient storing and retrieval of data. Data Visualization : Professionals with skills in data visualization technologies such as Tableau, Power BI, or matplotlib can show complex data in an intelligible manner.

Big Data 52

Big Data Certification Hadoop Scala 52

Monte Carlo

APRIL 15, 2022

Any actions taken to mitigate these anomalies should be applied across the data warehouse/data lake to promote healthy data observability habits. This way, individual teams have access to a clean view into their data without having to parse through the entire organization.

Bytes 52

Bytes Data Lake BI Data 52

Monte Carlo

JUNE 28, 2022

Since then, Databricks has aggressively moved toward allowing users to add more structure to their data. Features like the Delta Lake and Unity Catalog , help combine the best of both the data lake and data warehouse worlds (see: data lakehouse ). Leave a Reply Cancel reply Your email address will not be published.

Data Lake 52

Data Lake Metadata AWS Data Warehouse 52

Rockset

DECEMBER 9, 2021

Big firms from Amazon to Netflix already use instant data to monitor grocery deliveries and how many people are glued to “Squid Game”. The pandemic has led governments and central banks to experiment, from monitoring restaurant bookings to tracking card payments. Every layer in the modern data stack was built for a batch-based world.

Transportation 52

Transportation BI SQL Data Warehouse 52

Knowledge Hut

DECEMBER 26, 2023

What is the need for Data Science? If we look at history, the data that was generated earlier was primarily structured and small in its outlook. A simple usage of Business Intelligence (BI) would be enough to analyze such datasets. The spectrum of sources from which data is collected for the study in Data Science is broad.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

Knowledge Hut

DECEMBER 28, 2023

The certification path covers a comprehensive range of topics crucial for a successful career as an Azure Data Engineer: Data storage options like Azure Data Lake Storage and Azure SQL Data Warehouse Data processing solutions such as Azure Databricks and Azure Stream Analytics.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Monte Carlo

SEPTEMBER 23, 2021

Lineage: When data breaks, the first question is always “where?” Data lineage provides the answer by telling you which upstream sources and downstream ingestors were impacted, as well as which teams are generating the data and who is accessing it. Throughout this time, data is transformed, often more than once.

BI 52

BI Data Metadata Data Pipeline 52

AltexSoft

OCTOBER 21, 2022

After trying all options existing on the market — from messaging systems to ETL tools — in-house data engineers decided to design a totally new solution for metrics monitoring and user activity tracking which would handle billions of messages a day. How Apache Kafka streams relate to Franz Kafka’s books. Books and papers.

Kafka 93

Kafka Hadoop ETL Tools Big Data 93

Monte Carlo

JULY 13, 2021

Incident IQ is the first fully automated, end-to-end solution that conducts root cause analysis for data issues and changes at each stage of the pipeline, from ingestion in the data warehouse or lake to analytics in your business intelligence dashboards. Book a time to speak with us using the form below. EST / 9:00 a.m.

Food 40

Food Data Pipeline BI Business Intelligence 40

Monte Carlo

JULY 13, 2021

As architectures to control modern data environments continue to evolve, data observability solutions such as Monte Carlo will make trustworthy data more accessible and scalable.” PST to learn more, or book a time to speak with us using the form below! EST / 9:00 a.m.

Management 40

Management Data Pipeline Retail BI 40

Monte Carlo

OCTOBER 1, 2020

In this article we introduce “Key Assets”, a new approach taken by the best data teams to surface your most important data assets for quick and reliable insights. Have you been 3/4ths of the way done with a data warehouse migration only to discover that you don’t know which data assets are right and which ones are wrong?

Data Warehouse 40

Data Warehouse Datasets Data Validation Machine Learning 40

Monte Carlo

APRIL 29, 2021

With this partnership, Snowflake customers can now: Achieve end-to-end Data Observability without writing any code. Get end-to-end data observability for your Snowflake data pipelines with a 20-minute, no-code implementation process. Know when data breaks, as soon as it happens.

Insurance 40

Insurance Business Intelligence Healthcare Data Warehouse 40

Monte Carlo

AUGUST 10, 2023

Lineage : When data breaks, the first question is always “where?” Data lineage provides the answer by telling you which upstream sources and downstream ingestors were impacted, as well as which teams are generating the data and who is accessing it. The original article that launched a category: What is data observability?

Data Pipeline 98

Data Pipeline Software Engineer Software Engineering Machine Learning 98

Monte Carlo

JANUARY 23, 2023

They allow organizations to ingest and manage large volumes of data in an aggregated storage solution when they might not be entirely sure how they’ll use that data in the future, whether for business intelligence or data products. If a data lake is one approach to data architecture, a data mesh is its philosophical opposite.

Data Lake 52

Data Lake Unstructured Data Business Intelligence Architecture 52

Monte Carlo

MAY 31, 2022

Metadata from the data warehouse/lake and from the BI tool of record can then be used to map the dependencies between the tables and dashboards. It also becomes outdated virtually the moment it’s mapped as your environment continues to ingest more data and you continue to layer on additional solutions.

Data Lake 52

Data Lake Building Scala Metadata 52

Monte Carlo

APRIL 26, 2022

And in order to do this, business users need to have the ability to self-serve their own data needs. Rather than data teams fulfilling ad hoc requests from business users (which ultimately slows down decision-making), business stakeholders can access the data they need when they need it.

IT 64

IT Data Pipeline Data Engineering Data Engineer 64

Monte Carlo

JUNE 7, 2022

Developing internal data products–whether a high-powered executive dashboard, a machine learning powered predictive buyer model for marketing, or a new customer model for the BI team–is still one of the most powerful ways data teams can add value to the organization. What is the frequency, size, and required timeliness?

Building 52

Building Raw Data Architecture Machine Learning 52

Databand.ai

DECEMBER 20, 2022

Ken spreads this knowledge on his YouTube channel and on his Medium blog , where his content provides insight into the data science community, career advice, and sports related analysis. On LinkedIn, he posts regularly about data analytics and data science.

Data Analytics 52

Data Analytics Google Cloud Data Science Data Mining 52