Cloudera

AUGUST 17, 2022

What if you could access all your data and execute all your analytics in one workflow, quickly with only a small IT team? CDP One is a new service from Cloudera that is the first data lakehouse SaaS offering with cloud compute, cloud storage, machine learning (ML), streaming analytics, and enterprise grade security built-in.

Cloud Computing 115

Cloud Computing Cloud Storage Data Science Government 115

phData: Data Engineering

AUGUST 4, 2023

Data storage is a vital aspect of any Snowflake Data Cloud database. Within Snowflake, data can either be stored locally or accessed from other cloud storage systems. What are the Different Storage Layers Available in Snowflake? They are flexible, secure, and provide exceptional performance.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

DareData

JANUARY 30, 2023

Our goal is to help data scientists better manage their models deployments or work more effectively with their data engineering counterparts, ensuring their models are deployed and maintained in a robust and reliable way. DigDag: An open-source orchestrator for data engineering workflows.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

Cloudera

MARCH 31, 2021

One of our customers, Commerzbank, has used the CDP Public Cloud trial to prove that they can combine both Google Cloud and CDP to accelerate their migration to Google Cloud without compromising data security or governance. . Data Preparation (Apache Spark and Apache Hive) .

Google Cloud 92

Google Cloud Cloud Amazon Web Services Cloud Storage 92

Towards Data Science

MARCH 5, 2024

In that case, queries are still processed using the BigQuery compute infrastructure but read data from GCS instead. Such external tables come with some disadvantages but in some cases it can be more cost efficient to have the data stored in GCS. This data modelling technique can reduce the stored and processed bytes drastically.

Bytes 69

Bytes Google Cloud Cloud Storage Utilities 69

RandomTrees

SEPTEMBER 6, 2020

The architecture is three layered: Database Storage: Snowflake has a mechanism to reorganize the data into its internal optimized, compressed and columnar format and stores this optimized data in cloud storage. The data objects are accessible only through SQL query operations run using Snowflake.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 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 70

Data Warehouse Cloud Kafka Cloud Storage 70

Ascend.io

JUNE 6, 2023

But if reading is not your thing, dive right into the fascinating details of how we master cloud costs in the video below. Table of Contents Understanding What Drives Cloud Costs So, what exactly are the primary factors driving cloud costs within a typical data processing environment?

Cloud 52

Cloud Data Pipeline Data Ingestion Cloud Storage 52

AltexSoft

MAY 12, 2023

You may encounter unstructured data in the form of text documents, which can be plain text files (.txt), html), and other word processing formats. As a form of electronic communication, emails often contain unstructured text data and various file attachments, such as images, documents, or spreadsheets. doc,docx), PDF files (.pdf),

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Towards Data Science

NOVEMBER 4, 2023

The data engineering landscape is constantly changing but major trends seem to remain the same. How to Become a Data Engineer As a data engineer, I am tasked to design efficient data processes almost every day. It was created by Spotify to manage massive data processing workloads. Image by author.

Data Engineering 82

Data Engineering Data Engineer Engineering BI 82

Knowledge Hut

SEPTEMBER 26, 2023

Organisations are constantly looking for robust and effective platforms to manage and derive value from their data in the constantly changing landscape of data analytics and processing. These platforms provide strong capabilities for data processing, storage, and analytics, enabling companies to fully use their data assets.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

Monte Carlo

APRIL 24, 2023

By accommodating various data types, reducing preprocessing overhead, and offering scalability, data lakes have become an essential component of modern data platforms , particularly those serving streaming or machine learning use cases. Google Cloud Platform and/or BigLake Google offers a couple options for building data lakes.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

ProjectPro

AUGUST 24, 2021

Data Engineering Project for Beginners If you are a newbie in data engineering and are interested in exploring real-world data engineering projects, check out the list of data engineering project examples below. This big data project discusses IoT architecture with a sample use case.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Rockset

MARCH 1, 2023

As a result, compute contention ensues, causing several problems for customers and prospects: User-facing analytics in my SaaS application can only update every 30 minutes since the underlying database becomes unstable whenever I try to process streaming data continuously.

Architecture 59

Architecture AWS SQL Cloud Storage 59

Confluent

FEBRUARY 6, 2019

It allows real-time data ingestion, processing, model deployment and monitoring in a reliable and scalable way. This blog post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers.

Machine Learning 106

Machine Learning Python Kafka Java 106

Monte Carlo

OCTOBER 5, 2023

Technology alone falls short in delivering consistently reliable data, and without organizational support, even the most talented data teams will face an uphill climb when it comes to improving data quality. This is because data ecosystems are inherently complex, with diverse sources, formats, use cases, and end consumers.

Government 52

Government Data Governance Data Datasets 52

Monte Carlo

AUGUST 19, 2023

A data platform, often referred to as a ‘modern data stack,’ is the central processing hub for an organization’s data ecosystem. We’ll cover: What is a data platform? A data platform is a central repository and processing house for all of an organization’s data.

Building 52

Building BI Data Lake Data Governance 52

Cloudera

FEBRUARY 16, 2022

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.

Machine Learning 83

Machine Learning Algorithm Government Metadata 83

Cloudera

OCTOBER 15, 2020

In this blog we will go through the process of indexing data from S3 into Solr in DDE with the help of NiFi in Data Flow. The scenario is the same as it was in the previous blog but the ingest pipeline differs. Spark as the ingest pipeline tool for Search (i.e. Content Stream Path. Be careful of the leading “/”.

AWS 116

AWS Data Cloud Cloud Storage 116

Knowledge Hut

JUNE 26, 2023

If you want to break into the field of data engineering but don't yet have any expertise in the field, compiling a portfolio of data engineering projects may help. Data pipeline best practices should be shown in these initiatives. Source Code: Stock and Twitter Data Extraction Using Python, Kafka, and Spark 2.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

AltexSoft

MARCH 30, 2023

To dive deeper into details, read our article Data Lakehouse: Concept, Key Features, and Architecture Layers. The lakehouse platform was founded by the creators of Apache Spark , a processing engine for big data workloads. Besides that, it’s fully compatible with various data ingestion and ETL tools.

Scala 64

Scala Data Lake BI Google Cloud 64

WeCloudData

OCTOBER 19, 2021

The team was able to achieve this by leveraging cloud as well as open source tools in a modular set up, taking advantage of relatively cheap cloud storage, a versatile programming language in Python and Spark’s powerful processing engine.

Consulting 52

Consulting Raw Data Data Lake Data Pipeline 52

WeCloudData

OCTOBER 19, 2021

The team was able to achieve this by leveraging cloud as well as open source tools in a modular set up, taking advantage of relatively cheap cloud storage, a versatile programming language in Python and Spark’s powerful processing engine.

Consulting 52

Consulting Raw Data Data Lake Data Pipeline 52

Rockset

MARCH 15, 2021

While there’s typically some amount of data engineering required here, there are ways to minimize it. For example, instead of denormalizing the data, you could use a query engine that supports joins. This will avoid unnecessary processing during data ingestion and reduce the storage bloat due to redundant data.

MongoDB 40

MongoDB Data Ingestion Analytics Application Kafka 40

Monte Carlo

FEBRUARY 15, 2023

Since the inception of the cloud, there has been a massive push to store any and all data. On the surface, the promise of scaling storage and processing is readily available for databases hosted on AWS RDS, GCP cloud SQL and Azure to handle these new workloads. Cloud data warehouses solve these problems.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

Knowledge Hut

APRIL 25, 2023

Data engineering tools are software applications that help data engineers manage and process large and complex data sets. Data engineering is a field that requires a range of technical skills, including database management, data modeling, and programming. Let’s take a look: 1.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

Rockset

MARCH 21, 2019

This makes turning any type of data—from JSON, XML, Parquet, and CSV to even Excel files—into SQL tables a trivial pursuit. We automatically build multiple general-purpose indexes on all data ingested into Rockset, so that we can eliminate the need for database administration and query tuning for a wide spectrum of applications.

SQL 52

SQL Data Management Management Engineering 52

Knowledge Hut

JULY 6, 2023

It is a built-in Massively parallel processing (MPP) data lake house to handle all your infrastructure observability and security needs. However, there are costs associated with data ingestion. Logging and managing storage resources is effortless, making this tool popular among competitors.

Amazon Web Services 52

Amazon Web Services Data Lake Java SQL 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? A pipeline may include filtering, normalizing, and data consolidation to provide desired data.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

AltexSoft

JULY 29, 2022

Apache Hadoop is an open-source Java-based framework that relies on parallel processing and distributed storage for analyzing massive datasets. Developed in 2006 by Doug Cutting and Mike Cafarella to run the web crawler Apache Nutch, it has become a standard for Big Data analytics. What is Hadoop?

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Rockset

NOVEMBER 12, 2020

Elasticsearch is one tool to which reads can be offloaded, and, because both MongoDB and Elasticsearch are NoSQL in nature and offer similar document structure and data types, Elasticsearch can be a popular choice for this purpose. This blog post will examine the various tools that can be used to sync data between MongoDB and Elasticsearch.

MongoDB 40

MongoDB NoSQL Data Pipeline Data Storage 40

Knowledge Hut

AUGUST 18, 2023

With the rise of cloud computing, there’s no better time to explore the top Google Cloud Certifications that can take your career to new heights. Having gone through the process myself, I can attest to the immense value & recognition that comes with earning a Google Cloud Certification.

Google Cloud 52

Google Cloud Certification Cloud Cloud Computing 52

Netflix Tech

DECEMBER 14, 2018

MDVS also serves as the storehouse and the manager for the data schema itself. As was noted in the previous post , data schema could itself evolve over time, but all the data, ingested hitherto, has to remain compliant with the latest schema. This allows for a seamless self-service process for creating and managing a DS.

Media 94

Media Database Metadata Data Schemas 94

ProjectPro

MAY 31, 2021

Before starting any big data project, it is essential to become familiar with the fundamental processes and steps involved, from gathering raw data to creating a machine learning model to its effective implementation. Access Solution to Data Warehouse Design for an E-com Site 4.

Big Data 52

Big Data Coding Project Hadoop 52

Confluent

OCTOBER 16, 2019

My source of data is a public feed provided by the UK’s Network Rail company through an ActiveMQ interface. Additional data is available over REST as well as static reference data published on web pages. As with any system out there, the data often needs processing before it can be used. Wrangling the data.

Kafka 19

Kafka Building Data Coding 19