ProjectPro

FEBRUARY 8, 2023

But this data is not that easy to manage since a lot of the data that we produce today is unstructured. In fact, 95% of organizations acknowledge the need to manage unstructured raw data since it is challenging and expensive to manage and analyze, which makes it a major concern for most businesses. How Does AWS Glue Work?

AWS 98

AWS Scala Metadata Data Lake 98

Monte Carlo

MAY 28, 2024

Data Transformation : Clean, format, and convert extracted data to ensure consistency and usability for both batch and real-time processing. Data Loading : Load transformed data into the target system, such as a data warehouse or data lake.

Data Ingestion 52

Data Ingestion Architecture Designing Hadoop 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. best suit our processed data? cleaning, formatting)?

Data Pipeline 52

Data Pipeline Building Raw Data Data Warehouse 52

Data Engineering Podcast

JUNE 26, 2022

Summary The most complicated part of data engineering is the effort involved in making the raw data fit into the narrative of the business. RudderStack helps you build a customer data platform on your warehouse or data lake. RudderStack helps you build a customer data platform on your warehouse or data lake.

Data Management 100

Data Management Management MongoDB Scala 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

Monte Carlo

APRIL 24, 2023

During data ingestion, raw data is extracted from sources and ferried to either a staging server for transformation or directly into the storage level of your data stack—usually in the form of a data warehouse or data lake. There are two primary types of raw data.

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

ProjectPro

DECEMBER 7, 2021

Generally, data pipelines are created to store data in a data warehouse or data lake or provide information directly to the machine learning model development. Keeping data in data warehouses or data lakes helps companies centralize the data for several data-driven initiatives.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

AltexSoft

MAY 12, 2023

Unstructured data , on the other hand, is unpredictable and has no fixed schema, making it more challenging to analyze. Without a fixed schema, the data can vary in structure and organization. There are several widely used unstructured data storage solutions such as data lakes (e.g., Hadoop, Apache Spark).

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

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.

Data Lake 81

Data Lake Analytics Application Cloud Storage Architecture 81

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

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

AltexSoft

JUNE 26, 2023

Data collection revolves around gathering raw data from various sources, with the objective of using it for analysis and decision-making. It includes manual data entries, online surveys, extracting information from documents and databases, capturing signals from sensors, and more.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Ripple Engineering

MAY 27, 2021

At Ripple , we are moving towards building complex business models out of raw data. A prime example of this was the process of managing our data transformation workflows. This enables our analysts to focus on data curation and modelling rather than infrastructure. SQL Models A model is a single.sql file.

Building 52

Building Raw Data SQL Data 52

Monte Carlo

MARCH 14, 2023

Data from these sources are often ingested into a cloud-based data warehouse or data lake , where they can then be mined for information and insights. Source : Fundamentals of Data Engineering by Joe Reis and Matt Housley.

Data Ingestion 52

Data Ingestion Data Warehouse Lambda Architecture Raw Data 52

Snowflake

MARCH 30, 2023

Collecting, cleaning, and organizing data into a coherent form for business users to consume are all standard data modeling and data engineering tasks for loading a data warehouse. Based on Tecton blog So is this similar to data engineering pipelines into a data lake/warehouse?

Engineering 83

Engineering Raw Data Data Science Scala 83

Grouparoo

AUGUST 3, 2021

The mission of many data teams is a very simple one. They seek to use data to help the business take smarter actions. The input is raw data from everywhere that touches the business. These are the steps: Extract : Connect to the sources and read the valuable data. Files: Many types of other data exist.

Raw Data 52

Raw Data Data Warehouse SQL Coding 52

Rockset

AUGUST 30, 2021

Apache Kafka has made acquiring real-time data more mainstream, but only a small sliver are turning batch analytics, run nightly, into real-time analytical dashboards with alerts and automatic anomaly detection. The majority are still draining streaming data into a data lake or a warehouse and are doing batch analytics.

SQL 52

SQL Kafka MongoDB MySQL 52

Knowledge Hut

SEPTEMBER 27, 2023

Big Data support The SPICE engine in QuickSight was built to handle huge datasets, making it suited for big data scenarios. Its capacity to handle large amounts of data increases its flexibility in business settings. Imagine that your company arranges a virtual event, resulting in a spike in dashboard access.

BI 52

BI AWS Database-centric Data Lake 52

ProjectPro

FEBRUARY 16, 2023

ETL Architecture on AWS: Examining the Scalable Architecture for Data Transformation ETL Architecture on AWS typically consists of three components - Source Data Store A Data Transformation Layer Target Data Store Source Data Store The source data store is where raw data is stored before being transformed and loaded into the target data store.

AWS 52

AWS Data Management ETL Tools Management 52

Ascend.io

DECEMBER 11, 2023

Data pipeline orchestration is characterized by a detailed understanding of pipeline events and processes. In comparison, general data orchestration does not offer this degree of contextual insight Why Data Orchestration Is Important (But an Unnecessary Complication?) Not every team needs data orchestration.

Data Workflow 52

Data Workflow Data Pipeline Data Lake Data 52

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. Zero ETL is a bit of a misnomer.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Cloudera

JANUARY 21, 2021

Of high value to existing customers, Cloudera’s Data Warehouse service has a unique, separated architecture. . Cloudera’s Data Warehouse service allows raw data to be stored in the cloud storage of your choice (S3, ADLSg2). When your IT admin registers an environment in CDP, a Data Lake is automatically deployed.

IT 93

IT Data Lake Data Warehouse Cloud Storage 93

Rockset

AUGUST 25, 2021

They are an essential part of the modern data stack for powering: Real-time search applications Social features in the product Recommendation/rewards features in the product Real-time dashboards IoT applications These use cases can have several TBs per day streaming in - they are literally data torrents.

Aggregated Data 52

Aggregated Data Data Lake Hadoop SQL 52

Monte Carlo

AUGUST 19, 2023

With companies moving their data platforms to the cloud, the emergence of cloud-native solutions ( data warehouse vs data lake or even a data lakehouse ) have taken over the market, offering more accessible and affordable options for storing data relative to many on-premises solutions.

Building 52

Building BI Data Lake Data Governance 52

Data Engineering Weekly

JANUARY 15, 2023

However, the medallion architecture brings a clear bucketing of data to align with the organization's delivery strategy from raw data → filer & clean data, → business metrics. The names hold less meaning to the outcome, but its fancy. To achieve this outcome, the model needs to be calibrated.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Confluent

JUNE 7, 2019

Access to the data lake and raw data streams is self-provisioned which allows us to work in parallel, and to scale to support multiple protocols (e.g., On the event streaming side, we offer reliable low-latency data streams for financial applications based on Kafka Streams.

Accessible 90

Accessible Accessibility Kafka Scala 90

Monte Carlo

APRIL 4, 2023

Importantly, related data elements are linked so that they are a mirror to the operations in the real world. million) and events (Giants football game). Data is extracted, transformed, and loaded in the data warehouse like puzzle pieces to form this picture of reality.

Data Warehouse 52

Data Warehouse Data Governance Data Architecture 52

Ascend.io

SEPTEMBER 14, 2023

Python for Data Engineering Use Cases Data engineering, at its core, is about preparing “big data” for analytical processing. It’s an umbrella that covers everything from gathering raw data to processing and storing it efficiently.

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

Rockset

JANUARY 28, 2020

Schemaless Ingest for Running Experiments Standard builds their production systems to access the streams of events they collect through a number of backend APIs, and the team is continually adding new API endpoints to make more data available to developers. How accurate were the AI models compared to human-resolved events?

Retail 40

Retail Google Cloud Raw Data Data Lake 40

Knowledge Hut

OCTOBER 8, 2023

You must be proficient in NoSQL and SQL for data engineers to help with database management. Data pipeline design - It's where you extract raw data from different data sources and export it for analysis. Data engineers must design efficient pipelines for easy transfer of data.

AWS 52

AWS Data Engineering Data Engineer Engineering 52

Monte Carlo

JANUARY 12, 2023

All of the data lands in a single query space–a data lake is an appropriate term here– where it is available for analysis, research, or further activities such as data marting and summary creation. Recommendation : There are no raw data files in this setup.

Architecture 52

Architecture Building Data Warehouse BI 52

ProjectPro

JULY 27, 2021

Data engineers and data scientists work very closely together, but there are some differences in their roles and responsibilities. Data Engineer Data scientist The primary role is to design and implement highly maintainable database management systems. It is difficult to query the required unstructured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

AltexSoft

MAY 14, 2021

The best way to understand the idea behind Big Data analytics is to put it against regular data analytics. The analytics commonly takes place after a certain period of time or event. If you are an owner of an online shop, you may look at the data accumulated during a week and then analyze it. Data storage and processing.

Big Data 78

Big Data Data Analytics IT NoSQL 78

U-Next

AUGUST 25, 2022

IA is used to identify patterns in data sets containing data points that are neither classified nor labeled. Unsupervised learning is used in cluster analysis to filter through enormous raw data lakes and group certain data pieces. Semi-Supervised Learning.

Machine Learning 40

Machine Learning Algorithm Data Mining Utilities 40

ProjectPro

NOVEMBER 30, 2021

Now that we have understood how much significant role data plays, it opens the way to a set of more questions like How do we acquire or extract raw data from the source? How do we transform this data to get valuable insights from it? Where do we finally store or load the transformed data?

Process 52

Process Data Pipeline Data Warehouse AWS 52

ProjectPro

AUGUST 24, 2021

Within no time, most of them are either data scientists already or have set a clear goal to become one. Nevertheless, that is not the only job in the data world. And, out of these professions, this blog will discuss the data engineering job role. This big data project discusses IoT architecture with a sample use case.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

DataKitchen

DECEMBER 9, 2022

DataOps involves collaboration between data engineers, data scientists, and IT operations teams to create a more efficient and effective data pipeline, from the collection of raw data to the delivery of insights and results. The post An AI Chat Bot Wrote This Blog Post … first appeared on DataKitchen.

Machine Learning 52

Machine Learning Data Preparation Government Data Analytics 52