Data Engineering Podcast

SEPTEMBER 25, 2022

Summary Data integration from source systems to their downstream destinations is the foundational step for any data product. With the increasing expecation for information to be instantly accessible, it drives the need for reliable change data capture. In fact, while only 3.5% That’s where our friends at Ascend.io

Food 100

Food MongoDB Scala MySQL 100

Snowflake

MARCH 21, 2023

In February, Snowflake launched new features around streaming data ingestion and data governance and improved SQL experience and performance, with enhancements to Search Optimization Service and more. Read on to learn about everything new announced in February. Learn more here.

Retail 72

Retail Healthcare Data Ingestion Consulting 72

Knowledge Hut

MARCH 19, 2024

Security : AWS promotes security and compliance, providing comprehensive security features and controls to protect data and applications. These include encryption, identity and access management , network security, and compliance certifications. Conclusion AWS has released over two hundred production-level services.

AWS 52

AWS Cloud Computing Amazon Web Services Relational Database 52

Data Engineering Podcast

OCTOBER 9, 2022

In this episode Purvi Shah, the VP of Enterprise Big Data Platforms at American Express, explains how they have invested in the cloud to power this visibility and the complex suite of integrations they have built and maintained across legacy and modern systems to make it possible. In fact, while only 3.5%

Food 100

Food MongoDB Scala MySQL 100

Snowflake

SEPTEMBER 13, 2023

Snowpark External Access – public preview External Access is in public preview on AWS regions. Users can now easily connect to external network locations, including external LLMs, from their Snowpark code while maintaining high security and governance over their data. Snowpark Python Updates Snowpark support for Python 3.9

Python 77

Python SQL Data Pipeline Data Ingestion 77

Cloudera

APRIL 20, 2021

Streaming Analytics is a type of data analysis that processes data streams for real-time analytics. It continuously processes data from multiple streams and performs simple calculations to complex event processing for delivering sophisticated use cases. What are the advantages of Streaming Analytics?

Hospitality 92

Hospitality Kafka Retail Data Ingestion 92

Data Engineering Podcast

OCTOBER 2, 2022

Summary Data lineage is something that has grown from a convenient feature to a critical need as data systems have grown in scale, complexity, and centrality to business. Alvin is a platform that aims to provide a low effort solution for data lineage capabilities focused on simplifying the work of data engineers.

IT 100

IT Food PostgreSQL MongoDB 100

Databand.ai

JUNE 28, 2023

“Observability” has become a bit of a buzzword so it’s probably best to define it: Data observability is the blanket term for monitoring and improving the health of data within applications and systems like data pipelines. Data observability vs. monitoring: what is the difference?

Data Pipeline 98

Data Pipeline Data Engineering Data Engineer Engineering 98

Snowflake

AUGUST 16, 2023

Custom Event Billing capabilities enable you to build your own pricing strategy. You can charge customers based on their usage and specify billing events based on your preferences, such as consumed rows, ingested rows and more. What remains challenging is how streaming data is brought together with batch data.

Scala 52

Scala Transportation Kafka Data Lake 52

Data Engineering Weekly

JUNE 4, 2023

[link] Uber: Spark Analysers: Catching Anti-Patterns In Spark Apps One of the challenges in commoditizing data processing engines like Spark is that it requires an expert user to understand and operate this system. The future we wish to live in is the promise of vector search combined with SQL to access structured information.

Data Engineering 52

Data Engineering Data Engineer Engineering Medical 52

Ascend.io

JANUARY 2, 2024

The key differentiation lies in the transformational steps that a data pipeline includes to make data business-ready. Ultimately, the core function of a pipeline is to take raw data and turn it into valuable, accessible insights that drive business growth. Questions to Ask: What are all the potential sources of data?

Data Pipeline 52

Data Pipeline Building Raw Data Data Warehouse 52

Databand.ai

AUGUST 30, 2023

The core philosophy of DataOps is to treat data as a valuable asset that must be managed and processed efficiently. It emphasizes the importance of collaboration between different teams, such as data engineers, data scientists, and business analysts, to ensure that everyone has access to the right data at the right time.

Data Governance 52

Data Governance Data Pipeline Government Data Cleanse 52

Knowledge Hut

JUNE 26, 2023

When screening resumes, most hiring managers prioritize candidates who have actual experience working on data engineering projects. Top Data Engineering Projects with Source Code Data engineers make unprocessed data accessible and functional for other data professionals. Which queries do you have?

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Monte Carlo

MAY 26, 2023

One ecommerce platform lost around $5 million in revenue because their machine learning model that identified out of stock items and recommended substitutions was operating on thousands of temporary tables and stale data for six months. Pro-tip : Don’t confuse data freshness with data latency.

Data Pipeline 98

Data Pipeline Data Data Warehouse Machine Learning 98

AltexSoft

AUGUST 29, 2023

It is a versatile platform for exploring, refining, and analyzing petabytes of information that continually flow in from various data sources. Who needs a data lake? If the intricacies of big data are becoming too much for your existing systems to handle, a data lake might be the solution you’re seeking.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Confluent

FEBRUARY 6, 2019

The blog posts How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka and Using Apache Kafka to Drive Cutting-Edge Machine Learning describe the benefits of leveraging the Apache Kafka ® ecosystem as a central, scalable and mission-critical nervous system. You need to think about the whole model lifecycle.

Machine Learning 107

Machine Learning Python Kafka Java 107

Pinterest Engineering

AUGUST 18, 2022

The HBase Ecosystem, though having various advantages like strong consistency at row level in high volume requests, flexible schema, low latency access to data, Hadoop integration, etc. However, the speed offered by TiDB backend mode is 50GB/hour so it’s only useful for data migration of smaller tables.

Data Ingestion 52

Data Ingestion Hadoop Database Kafka 52

Cloudera

FEBRUARY 9, 2021

Today’s customers have a growing need for a faster end to end data ingestion to meet the expected speed of insights and overall business demand. This ‘need for speed’ drives a rethink on building a more modern data warehouse solution, one that balances speed with platform cost management, performance, and reliability.

Data Warehouse 69

Data Warehouse Cloud Kafka Cloud Storage 69

DareData

JULY 5, 2023

The Data Warehouse(s) facilitates data ingestion and enables easy access for end-users. This is the first layer where data may be structured around the typical row-column format. The Data Mart(s) contain the aggregated layer where data is modeled according to specific business needs.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

Rockset

JULY 28, 2022

CDC enables true real-time analytics on your application data, assuming the platform you send the data to can consume the events in real time. Options For Change Data Capture on MongoDB Apache Kafka The native CDC architecture for capturing change events in MongoDB uses Apache Kafka.

MongoDB 52

MongoDB Kafka NoSQL Data Lake 52

Rockset

MAY 17, 2022

This is the fourth post in a series by Rockset's CTO Dhruba Borthakur on Designing the Next Generation of Data Systems for Real-Time Analytics. For instance, customer personalization systems need to combine historic data sets with real-time data streams to instantly provide the most relevant product recommendations to customers.

SQL 52

SQL NoSQL Hadoop MongoDB 52

Rockset

AUGUST 30, 2021

The majority are still draining streaming data into a data lake or a warehouse and are doing batch analytics. That’s because traditional OLTP systems and data warehouses are ill-equipped to power real-time analytics easily or efficiently. In example above, these base aggregate metrics are count(*) and sum(error_flag).

SQL 52

SQL Kafka MongoDB MySQL 52

ProjectPro

FEBRUARY 16, 2023

A heavy forwarder is necessary in some use cases when data needs to be parsed before sending or when data needs to be forwarded depending on criteria like source or type of event. How do you compress data in Splunk? When sending over the TLS protocol, forwarders and HTTP Event Collectors compress data.

Big Data 52

Big Data Big Data Tools Cloud Data 52

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? An ETL pipeline is a series of procedures that comprises extracting and transforming data from a data source.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Monte Carlo

MARCH 14, 2023

Data ingestion is the process of collecting data from various sources and moving it to your data warehouse or lake for processing and analysis. It is the first step in modern data management workflows. Table of Contents What is Data Ingestion? Decision making would be slower and less accurate.

Data Ingestion 52

Data Ingestion Data Warehouse Lambda Architecture Raw Data 52

Cloudera

AUGUST 31, 2021

Accenture’s Smart Data Transition Toolkit leverages six proprietary accelerators to reduce the cost of CDP migration by as much as forty percent (40%). Each of these accelerators support multiple legacy systems, including Teradata, Netezza, Oracle, etc. Smart DwH Mover helps in accelerating data warehouse migration.

Data Warehouse 80

Data Warehouse Database-centric Metadata Cloud 80

Cloudera

JUNE 28, 2022

We will also use the information produced by the streaming analytics jobs to feed different downstream systems and dashboards. . The card transaction data usually comes from event-driven sources, where new data arrives as card purchases happen in the real world. Feeding streams to other systems. The use case.

Process 81

Process Kafka SQL Machine Learning 81

Rockset

JULY 29, 2022

This consistency promotes data integrity, so you can trust the insights to make informed decisions. Additionally, data warehouses are great at offering historical intelligence. However, organizations today are moving beyond just batch analytics on historical data. The Snowpipe feature manages continuous data ingestion.

Data Analytics 52

Data Analytics Data Warehouse Datasets Google Cloud 52

Maxime Beauchemin

JANUARY 20, 2017

In relation to previously existing roles , the data engineering field could be thought of as a superset of business intelligence and data warehousing that brings more elements from software engineering. Those systems have been taught to normalize the data for storage on their own.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

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. What is Data Engineering? We have prepared a how-to be data engineer roadmap for you.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Rockset

MAY 3, 2023

lower latency than Elasticsearch for streaming data ingestion. We’ll also delve under the hood of the two databases to better understand why their performance differs when it comes to search and analytics on high-velocity data streams. Why measure streaming data ingestion?

Data Ingestion 59

Data Ingestion Kafka Database Architecture 59

Monte Carlo

JULY 10, 2023

Missing values Incomplete data can sometimes look like missing values throughout a data set, such as: Missing salary numbers in HR data sets Missing phone numbers in a CRM Missing location data in a marketing campaign Missing blood pressure data in an EMR Missing transaction in a credit ledger And the list goes on.

Data Collection 40

Data Collection Data Governance Government Data 40

Rockset

OCTOBER 11, 2022

Introduction Managing streaming data from a source system, like PostgreSQL, MongoDB or DynamoDB, into a downstream system for real-time analytics is a challenge for many teams. For a system like Elasticsearch , engineers need to have in-depth knowledge of the underlying architecture in order to efficiently ingest streaming data.

Data Ingestion 52

Data Ingestion Kafka Relational Database PostgreSQL 52

Snowflake

APRIL 18, 2024

Legacy security information and event management (SIEM) solutions, like Splunk, are powerful tools for managing and analyzing machine-generated data. Legacy SIEM cost factors to keep in mind Data ingestion: Traditional SIEMs often impose limits to data ingestion and data retention.

Data Lake 78

Data Lake Data Ingestion Bytes Cloud Computing 78

Knowledge Hut

AUGUST 18, 2023

Also, certified professionals enjoy various benefits such as access to Google Cloud events & resources, invitations to forums, & a digital badge that verifies their proficiency. Even, you can also expect a higher salary, recognition from industry leaders, & access to premium Google Cloud resources.

Google Cloud 52

Google Cloud Certification Cloud Cloud Computing 52

Zalando Engineering

OCTOBER 23, 2017

Smart Products Smart Product Platform (SPP) is Zalando’s new product platform which enables the collection, management and exposure of fashion product data across the organization, at scale. This is done in conjunction with our Fashion Librarians (data stewardships) curation guidelines.

Media 40

Media Manufacturing Data Ingestion Metadata 40

Rockset

APRIL 29, 2019

In order to support analytical queries, developers typically use a multitude of different systems in conjunction with DynamoDB. DynamoDB + Glue + S3 + Athena One approach is to extract, transform, and load the data from DynamoDB into Amazon S3, and then use a service like Amazon Athena to run queries over it.

NoSQL 52

NoSQL PostgreSQL AWS SQL 52