Data Engineering Podcast

MARCH 22, 2021

Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. RudderStack’s smart customer data pipeline is warehouse-first.

Data Warehouse 100

Data Warehouse Metadata Data Lake Hadoop 100

Monte Carlo

MAY 18, 2023

We’ve been open about this work, providing very technical deep dives into how we developed column-level lineage and even some of our failures in building Spark data lineage for data lakes. The Spark data lake lineage example is helpful for context here. Why do you need data lineage? “I

Metadata 64

Metadata Data Lake Government Data Governance 64

Rockset

APRIL 26, 2023

Offline feature store : Detecting anomalies requires historical data in order to have a baseline for comparisons. This data tends to be slow changing and is stored in an offline feature store. This could be a cloud data warehouse, a data lake, or a database. The database has two primary jobs.

Architecture 52

Architecture Transportation Data Lake Insurance 52

Netflix Tech

MARCH 7, 2024

Data: Fast Data Our main data lake is hosted on S3, organized as Apache Iceberg tables. For ETL and other heavy lifting of data, we mainly rely on Apache Spark. Explainer flow is event-triggered by an upstream flow, such Model A, B, C flows in the illustration. ETL workflows), as well as downstream (e.g.

Systems 90

Systems Media Machine Learning Data Warehouse 90

Grouparoo

OCTOBER 6, 2021

This provided a nice overview of the breadth of topics that are relevant to data engineering including data warehouses/lakes, pipelines, metadata, security, compliance, quality, and working with other teams. It is made up of 97 small vignettes that are 2-3 pages each. The articles are in alphabetical order.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Data Engineering Podcast

AUGUST 6, 2022

Summary The optimal format for storage and retrieval of data is dependent on how it is going to be used. For analytical systems there are decades of investment in data warehouses and various modeling techniques. RudderStack helps you build a customer data platform on your warehouse or data lake.

Machine Learning 100

Machine Learning Database MySQL PostgreSQL 100

Hepta Analytics

FEBRUARY 14, 2022

This week, we got to think about our data ingestion design. We looked at the following: How do we ingest – ETL vs ELT Where do we store the data – Data lake vs data warehouse Which tool to we use to ingest – cronjob vs workflow engine NOTE : This weeks task requires good internet speed and good compute.

Data Ingestion 52

Data Ingestion Data Engineering Data Engineer Engineering 52

ProjectPro

FEBRUARY 16, 2023

With so much riding on the efficiency of ETL processes for data engineering teams, it is essential to take a deep dive into the complex world of ETL on AWS to take your data management to the next level. ETL has typically been carried out utilizing data warehouses and on-premise ETL tools.

AWS 52

AWS Data Management ETL Tools Management 52

Cloudera

SEPTEMBER 15, 2022

July brings summer vacations, holiday gatherings, and for the first time in two years, the return of the Massachusetts Institute of Technology (MIT) Chief Data Officer symposium as an in-person event. A key area of focus for the symposium this year was the design and deployment of modern data platforms. What is a data fabric?

Data Lake 79

Data Lake Analytics Application Cloud Storage Architecture 79

Striim

JANUARY 4, 2023

Companies need to analyze large volumes of datasets, leading to an increase in data producers and consumers within their IT infrastructures. These companies collect data from production applications and B2B SaaS tools (e.g., This data makes its way into a data repository, like a data warehouse (e.g.,

PostgreSQL 52

PostgreSQL Data Warehouse Data Lake Data 52

Snowflake

NOVEMBER 2, 2023

Over the years, the technology landscape for data management has given rise to various architecture patterns, each thoughtfully designed to cater to specific use cases and requirements. These patterns include both centralized storage patterns like data warehouse , data lake and data lakehouse , and distributed patterns such as data mesh.

Data Lake 89

Data Lake Data Warehouse Cloud Unstructured Data 89

ProjectPro

AUGUST 11, 2021

“Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Data Warehouse Architecture What is a Data lake?

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 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

Data Engineering Podcast

JUNE 17, 2023

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. What are the most interesting, innovative, or unexpected ways that you have seen column-aware data modeling used?

Data Lake 130

Data Lake Metadata Data Architecture Machine Learning 130

Towards Data Science

FEBRUARY 9, 2024

In truth, the synergy between batch and streaming pipelines is essential for tackling the diverse challenges posed to your data platform at scale. The key to seamlessly addressing these challenges lies, unsurprisingly, in data orchestration. Their robust core offering seamlessly integrates data warehouses with data-hungry applications.

Building 82

Building Transportation Data Lake Metadata 82

Data Engineering Podcast

NOVEMBER 20, 2022

Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. RudderStack helps you build a customer data platform on your warehouse or data lake. What is the workflow for someone getting Sifflet integrated into their data stack?

Data Lake 130

Data Lake Data Ingestion MongoDB Scala 130

Precisely

JANUARY 25, 2024

There are three potential approaches to mainframe modernization: Data Replication creates a duplicate copy of mainframe data in a cloud data warehouse or data lake, enabling high-performance analytics virtually in real time, without negatively impacting mainframe performance. Best Practice 2.

Metadata 69

Metadata Data Governance Relational Database Government 69

ProjectPro

FEBRUARY 8, 2023

It offers users a data integration tool that organizes data from many sources, formats it, and stores it in a single repository, such as data lakes, data warehouses, etc., Glue uses ETL jobs for extracting data from various AWS cloud services and integrating it into data warehouses and lakes.

AWS 98

AWS Scala Metadata Data Lake 98

Cloudera

JANUARY 21, 2021

While cloud-native, point-solution data warehouse services may serve your immediate business needs, there are dangers to the corporation as a whole when you do your own IT this way. Cloudera Data Warehouse (CDW) is here to save the day! CDW is an integrated data warehouse service within Cloudera Data Platform (CDP).

IT 93

IT Data Lake Data Warehouse Cloud Storage 93

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.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Data Engineering Weekly

DECEMBER 4, 2022

The author narrates why the data models are still important for managing data assets' structure, content, and relationships but also need to keep agility in mind to bring business velocity. The article highlights the challenges of maintaining data models in a world where SQL data warehouses are no longer the primary data platform.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

AltexSoft

DECEMBER 23, 2022

It is a data integration process with which you first extract raw information (in its original formats) from various sources and load it straight into a central repository such as a cloud data warehouse , a data lake , or a data lakehouse where you transform it into suitable formats for further analysis and reporting.

Process 52

Process Building Raw Data Data Lake 52

Data Engineering Weekly

JANUARY 15, 2023

SiliconANGLE theCUBE: Analyst Predictions 2023 - The Future of Data Management By far one of the best analyses of trends in Data Management. 2023 predictions from the panel are; Unified metadata becomes kingmaker. RudderStack builds your CDP on top of your data warehouse, giving you a more secure and cost-effective solution.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

AltexSoft

AUGUST 22, 2022

A data fabric is an architecture design presented as an integration and orchestration layer built on top of multiple disjointed data sources like relational databases , data warehouses , data lakes, data marts , IoT , legacy systems, etc., to provide a unified view of all enterprise data.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

AltexSoft

SEPTEMBER 23, 2021

One of the innovative ways to address this problem is to build a data hub — a platform that unites all your information sources under a single umbrella. This article explains the main concepts of a data hub, its architecture, and how it differs from data warehouses and data lakes. What is Data Hub?

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

Monte Carlo

AUGUST 10, 2023

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. At its core, there are three pillars of observability data : Metrics refer to a numeric representation of data measured over time.

Data Pipeline 98

Data Pipeline Software Engineer Software Engineering Machine Learning 98

AltexSoft

NOVEMBER 7, 2022

Metadata database. A metadata database stores information about user permissions, past and current DAG and task runs, DAG configurations, and more. By default, Airflow handles metadata with SQLite which is meant for development only. The Good and the Bad of Power BI Data Visualization. Since the 2.0

PostgreSQL 52

PostgreSQL Metadata Python MySQL 52

AltexSoft

JUNE 26, 2023

Data collection methods and tools: how to collect For centuries, people have been collecting data manually. Even now, in the era of ChatGPT , we still fill out paper-based documents and type numbers and words into an Excel file to capture events and record observations. The difference between data warehouses, lakes, and marts.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Rockset

NOVEMBER 28, 2022

It works with existing streaming systems like Apache Kafka, Amazon Kinesis, and Azure Events Hubs, making it easier than ever to build a real-time data pipeline. Data warehouses were built for batch jobs, so we shouldn’t be surprised by this. This method has a handful of drawbacks. The upshot?

Data Warehouse 52

Data Warehouse PostgreSQL MongoDB Data Pipeline 52

Monte Carlo

AUGUST 16, 2022

With Monte Carlo, data engineering teams can proactively manage the health of their data across the entire data stack — including data warehouses, data lakes, BI tools, data orchestration tools, and of course dbt — on a single platform.

Engineering 52

Engineering Data Lake Retail BI 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. There have been relational databases, data warehouses, data lakes, and even a combination of the latter two.

Architecture 52

Architecture Data Lake Medical Datasets 52

AltexSoft

OCTOBER 8, 2021

Data integration defines the process of collecting data from a number of disparate source systems and presenting it in a unified form within a centralized location like a data warehouse. So, why is data integration such a big deal? Connections to both data warehouses and data lakes are possible in any case.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

ProjectPro

JANUARY 31, 2022

As the demand for big data grows, an increasing number of businesses are turning to cloud data warehouses. The cloud is the only platform to handle today's colossal data volumes because of its flexibility and scalability. Launched in 2014, Snowflake is one of the most popular cloud data solutions on the market.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

ProjectPro

AUGUST 24, 2021

Data Warehousing: Data warehousing utilizes and builds a warehouse for storing data. A data engineer interacts with this warehouse almost on an everyday basis. Data Analytics: A data engineer works with different teams who will leverage that data for business solutions.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

ProjectPro

NOVEMBER 30, 2021

In extract-transform-load (ETL), data is obtained from multiple sources, transformed, and stored in a single data warehouse, with access to data analysts , data scientists , and business analysts for data visualization and statistical analysis model building, forecasting, etc.

Process 52

Process Data Pipeline Data Warehouse AWS 52

ProjectPro

NOVEMBER 15, 2021

It serves as a distributed processing engine for both categories of data streams: unbounded and bounded. Support for stream and batch processing, comprehensive state management, event-time processing semantics, and consistency guarantee for the state are just a few of Flink's capabilities.

Big Data 52

Big Data Project Metadata Programming Language 52

Snowflake

MARCH 30, 2023

A 2016 data science report from data enrichment platform CrowdFlower found that data scientists spend around 80% of their time in data preparation (collecting, cleaning, and organizing of data) before they can even begin to build machine learning (ML) models to deliver business value.

Engineering 82

Engineering Raw Data Data Science Scala 82