Data Engineering Podcast

FEBRUARY 13, 2022

To simplify the work of managing the full flow of your customer data and keep you in full control the team at Rudderstack created their eponymous open source platform that allows you to work with first and third party data, as well as build and manage reverse ETL workflows. Email hosts@dataengineeringpodcast.com ) with your story.

Building 100

Building Hadoop Data Pipeline Metadata 100

Cloudera

MAY 18, 2021

Prior the introduction of CDP Public Cloud, many organizations that wanted to leverage CDH, HDP or any other on-prem Hadoop runtime in the public cloud had to deploy the platform in a lift-and-shift fashion, commonly known as “Hadoop-on-IaaS” or simply the IaaS model. Introduction. Acknowledgment.

Hadoop 85

Hadoop Cloud AWS Utilities 85

Christophe Blefari

MARCH 1, 2023

dbt was born out of the analysis that more and more companies were switching from on-premise Hadoop data infrastructure to cloud data warehouses. You can also add metadata on models (in YAML). In this resource hub I'll mainly focus on dbt Core— i.e. dbt. First let's understand why dbt exists.

Data Warehouse 278

Data Warehouse SQL Metadata Raw Data 278

LinkedIn Engineering

DECEMBER 19, 2023

Co-authors: Arjun Mohnot , Jenchang Ho , Anthony Quigley , Xing Lin , Anil Alluri , Michael Kuchenbecker LinkedIn operates one of the world’s largest Apache Hadoop big data clusters. Historically, deploying code changes to Hadoop big data clusters has been complex.

Big Data 92

Big Data Hadoop Metadata Data 92

LinkedIn Engineering

MARCH 21, 2023

One of the most exciting parts of our work is that we get to play a part in helping progress a skills-first labor market through our team’s ongoing engineering work in building our Skills Graph. Engineering vs PyTorch Figure 6: Sample Seed Skills Graph KGBert helps build a more accurate and complex taxonomy in less time.

Building 85

Building Recruitment Machine Learning Deep Learning 85

Cloudera

AUGUST 26, 2021

Ozone natively provides Amazon S3 and Hadoop Filesystem compatible endpoints in addition to its own native object store API endpoint and is designed to work seamlessly with enterprise scale data warehousing, machine learning and streaming workloads. Data ingestion through ‘s3’. As described above, Ozone introduces volumes to the world of S3.

Data Science 111

Data Science Cloud Hadoop Metadata 111

ProjectPro

JUNE 22, 2017

What is a Hadoop Cluster? “A hadoop cluster is a collection of independent components connected through a dedicated network to work as a single centralized data processing resource. Table of Contents What is a Hadoop Cluster? Hadoop cluster setup is inexpensive as they are held down by cheap commodity hardware.

Hadoop 52

Hadoop IT Data Analysis Big Data 52

ProjectPro

JUNE 14, 2017

Hadoop was first made publicly available as an open source in 2011, since then it has undergone major changes in three different versions. Apache Hadoop 3 is round the corner with members of the Hadoop community at Apache Software Foundation still testing it. The major release of Hadoop 3.x x vs. Hadoop 3.x

Hadoop 52

Hadoop Java Big Data Coding 52

Data Engineering Podcast

MAY 18, 2021

In order to eliminate the wasted effort of building custom integrations every time you want to combine lineage information across systems Julien Le Dem introduced the OpenLineage specification. What is the current state of the ecosystem for generating and sharing metadata between systems? What are your goals for the OpenLineage effort?

Metadata 100

Metadata Kafka Data Warehouse Hadoop 100

ProjectPro

JANUARY 12, 2016

Choosing the right Hadoop Distribution for your enterprise is a very important decision, whether you have been using Hadoop for a while or you are a newbie to the framework. Different Classes of Users who require Hadoop- Professionals who are learning Hadoop might need a temporary Hadoop deployment.

Hadoop 52

Hadoop Big Data Metadata Java 52

ProjectPro

NOVEMBER 6, 2015

Hadoop has continued to grow and develop ever since it was introduced in the market 10 years ago. Every new release and abstraction on Hadoop is used to improve one or the other drawback in data processing, storage and analysis. Apache Hive is an abstraction on Hadoop MapReduce and has its own SQL like language HiveQL.

Hadoop 52

Hadoop SQL Java Metadata 52

Snowflake

MARCH 29, 2023

Iceberg supports many catalog implementations: Hive, AWS Glue, Hadoop, Nessie, Dell ECS, any relational database via JDBC, REST, and now Snowflake. After making an initial connection to Snowflake via the Iceberg Catalog SDK, Spark can read Iceberg metadata and Parquet files directly from the customer-managed storage account.

Metadata 96

Metadata Scala Hadoop Relational Database 96

ProjectPro

JANUARY 31, 2023

Typically, data processing is done using frameworks such as Hadoop, Spark, MapReduce, Flink, and Pig, to mention a few. How is Hadoop related to Big Data? Explain the difference between Hadoop and RDBMS. Data Variety Hadoop stores structured, semi-structured and unstructured data. Hardware Hadoop uses commodity hardware.

Big Data 52

Big Data Hadoop AWS Relational Database 52

Data Engineering Podcast

DECEMBER 16, 2019

Summary Building clean datasets with reliable and reproducible ingestion pipelines is completely useless if it’s not possible to find them and understand their provenance. The solution to discoverability and tracking of data lineage is to incorporate a metadata repository into your data platform.

Metadata 100

Metadata PostgreSQL Datasets Data Warehouse 100

Data Engineering Weekly

DECEMBER 29, 2022

Data catalogs are the most expensive data integration systems you never intended to build. Data Catalog as a passive web portal to display metadata requires significant rethinking to adopt modern data workflow, not just adding “modern” in its prefix. There are not many sources to pull the metadata. The modern(?)

Metadata 62

Metadata Data Warehouse ETL Tools Data Workflow 62

Data Engineering Weekly

JUNE 29, 2023

Databricks and Snowflake are better places to index the data and its metadata to enable natural language query capabilities. The question remains how far the data catalog tools can go with just the metadata. Snowflake and Databricks are racing to build the data intelligence layer on top of the cloud infrastructure layer.

Data Engineering 76

Data Engineering Data Engineer Google Cloud Engineering 76

ProjectPro

APRIL 20, 2017

You have read some of the best Hadoop books , taken online hadoop training and done thorough research on Hadoop developer job responsibilities – and at long last, you are all set to get real-life work experience as a Hadoop Developer.

Hadoop 40

Hadoop Big Data Coding Project 40

Monte Carlo

APRIL 24, 2023

However, one of the biggest trends in data lake technologies, and a capability to evaluate carefully, is the addition of more structured metadata creating “lakehouse” architecture. Amazon S3 and/or Lake Formation Amazon S3 is a popular storage platform to build and store data lakes thanks to its high availability and low latency access.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Data Engineering Podcast

MARCH 22, 2021

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. How do you handle observability of CDC flows?

Data Warehouse 100

Data Warehouse Metadata Data Lake Hadoop 100

ProjectPro

FEBRUARY 18, 2016

With big data seeping into every facet of our lives, we are trying to build robust systems which can process petabytes of data in a fast and accurate manner. Apache Hadoop, an open source framework is used widely for processing gigantic amounts of unstructured data on commodity hardware. What are Watches and Notification?

Hadoop 40

Hadoop Management Metadata Java 40

Data Engineering Podcast

OCTOBER 14, 2018

Summary With the growth of the Hadoop ecosystem came a proliferation of implementations for the Hive table format. This is a highly detailed and technical exploration of how a well-engineered metadata layer can improve the speed, accuracy, and utility of large scale, multi-tenant, cloud-native data platforms.

Data Lake 100

Data Lake Big Data Cloud Hadoop 100

AltexSoft

JULY 18, 2023

With its native support for in-memory distributed processing and fault tolerance, Spark empowers users to build complex, multi-stage data pipelines with relative ease and efficiency. It’s a powerful tool for social network analysis, PageRank algorithms, and building recommendation systems. Directed Acyclic Graph (DAG).

Big Data 52

Big Data Data Process Process Hadoop 52

Cloudera

MARCH 9, 2021

In this blog, we’ll highlight the key CDP aspects that provide data governance and lineage and show how they can be extended to incorporate metadata for non-CDP systems from across the enterprise. Atlas provides open metadata management and governance capabilities to build a catalog of all assets, and also classify and govern these assets.

Data Governance 95

Data Governance Government Metadata Datasets 95

AltexSoft

FEBRUARY 11, 2023

It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.); Feel free to enjoy it.

Data Architect 87

Data Architect Certification Generalist Big Data 87

Data Engineering Podcast

JUNE 8, 2021

Summary The way to build maintainable software and systems is through composition of individual pieces. In this episode Matteo Merli shares the story behind the creation of BookKeeper, the various ways that it is being used today, and the architectural aspects that make it such a strong building block for projects such as Pulsar.

Data Warehouse 100

Data Warehouse Hadoop Metadata Architecture 100

ProjectPro

MARCH 23, 2015

In one of our previous articles we had discussed about Hadoop 2.0 YARN framework and how the responsibility of managing the Hadoop cluster is shifting from MapReduce towards YARN. In one of our previous articles we had discussed about Hadoop 2.0 Here we will highlight the feature - high availability in Hadoop 2.0

Hadoop 40

Hadoop Big Data Architecture Metadata 40

AltexSoft

DECEMBER 23, 2022

Whether your goal is data analytics or machine learning , success relies on what data pipelines you build and how you do it. One of the ways to overcome challenges and gain more opportunities in terms of data integration is to build an ELT (Extract, Load, Transform) pipeline. Tools to build an ELT pipeline. What is ELT?

Process 52

Process Building Raw Data Data Lake 52

ProjectPro

NOVEMBER 7, 2016

Understanding the Hadoop architecture now gets easier! This blog will give you an indepth insight into the architecture of hadoop and its major components- HDFS, YARN, and MapReduce. We will also look at how each component in the Hadoop ecosystem plays a significant role in making Hadoop efficient for big data processing.

Hadoop 40

Hadoop Architecture IT Big Data 40

ProjectPro

OCTOBER 31, 2016

Having complete diverse big data hadoop projects at ProjectPro, most of the students often have these questions in mind – “How to prepare for a Hadoop job interview?” ” “Where can I find real-time or scenario-based hadoop interview questions and answers for experienced?” were excluded.).

Hadoop 52

Hadoop Big Data Utilities NoSQL 52

ProjectPro

OCTOBER 28, 2015

Apache Hadoop is synonymous with big data for its cost-effectiveness and its attribute of scalability for processing petabytes of data. Data analysis using hadoop is just half the battle won. Getting data into the Hadoop cluster plays a critical role in any big data deployment. then you are on the right page.

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52

Data Engineering Weekly

FEBRUARY 18, 2024

Modern data stack vendors chose speed, and never attempted to truly build something together. One can’t deny the role of Redshift in bringing the cloud data warehouse to the masses, starting the end of the Big Data era with Hadoop. I believe the data ownership problem is much deeper than simple metadata management.

Data Engineering 62

Data Engineering Data Engineer Engineering Data 62

Cloudera

APRIL 22, 2021

Cloudera has partnered with Cisco in helping build the Cisco Validated design (CVD) for Apache Ozone. Collects and aggregates metadata from components and present cluster state. Metadata in cluster is disjoint across components. Cloudera and Cisco have tested together with dense storage nodes to make this a reality. .

Pipeline-centric 100

Pipeline-centric Data Lake Hadoop Metadata 100

ProjectPro

APRIL 11, 2016

Let’s face it; the Hadoop Interview process is a tough cookie to crumble. If you are planning to pursue a job in the big data domain as a Hadoop developer , you should be prepared for both open-ended interview questions and unique technical hadoop interview questions asked by the hiring managers at top tech firms.

Hadoop 40

Hadoop Big Data Java Unstructured Data 40

Monte Carlo

AUGUST 25, 2023

A warehouse can be a one-stop solution, where metadata, storage, and compute components come from the same place and are under the orchestration of a single vendor. For metadata organization, they often use Hive, Amazon Glue, or Databricks. Storage can utilize S3, Google Cloud Storage, Microsoft Azure Blob Storage, or Hadoop HDFS.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

ProjectPro

APRIL 4, 2015

With the help of our best in class Hadoop faculty, we have gathered top Hadoop developer interview questions that will help you get through your next Hadoop job interview. IT organizations from various domains are investing in big data technologies, increasing the demand for technically competent Hadoop developers.

Hadoop 40

Hadoop Big Data Recruitment Java 40

ProjectPro

AUGUST 11, 2016

When people talk about big data analytics and Hadoop, they think about using technologies like Pig, Hive , and Impala as the core tools for data analysis. R and Hadoop combined together prove to be an incomparable data crunching tool for some serious big data analytics for business. Table of Contents Why use R on Hadoop?

Hadoop 40

Hadoop Big Data R (Programming) Programming Language 40

ProjectPro

OCTOBER 15, 2014

Pig and Hive are the two key components of the Hadoop ecosystem. What does pig hadoop or hive hadoop solve? Pig hadoop and Hive hadoop have a similar goal- they are tools that ease the complexity of writing complex java MapReduce programs. Apache HIVE and Apache PIG components of the Hadoop ecosystem are briefed.

Hadoop 52

Hadoop Unstructured Data Java SQL 52