Ascend.io

MAY 31, 2023

Data Ingestion Data ingestion is the first step of both ETL and data pipelines. In the ETL world, this is called data extraction, reflecting the initial effort to pull data out of source systems. The data sources themselves are not built to perform analytics.

Data Pipeline 52

Data Pipeline ETL Tools Pipeline-centric Data Warehouse 52

Knowledge Hut

DECEMBER 26, 2023

These skills are essential to collect, clean, analyze, process and manage large amounts of data to find trends and patterns in the dataset. The dataset can be either structured or unstructured or both. In this article, we will look at some of the top Data Science job roles that are in demand in 2024.

Data Science 97

Data Science BI Machine Learning Business Intelligence 97

Grouparoo

OCTOBER 26, 2021

This includes the different possible sources of data such as application APIs, social media, relational databases, IoT device sensors, and data lakes. This may include a data warehouse when it’s necessary to pipeline data from your warehouse to various destinations as in the case of a reverse ETL pipeline.

Data Pipeline 52

Data Pipeline ETL Tools Data Warehouse ETL System 52

Ascend.io

DECEMBER 19, 2023

We’ll talk about when and why ETL becomes essential in your Snowflake journey and walk you through the process of choosing the right ETL tool. Our focus is to make your decision-making process smoother, helping you understand how to best integrate ETL into your data strategy. But first, a disclaimer.

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ETL Tools IT Data Pipeline Data Warehouse 52

Monte Carlo

SEPTEMBER 8, 2021

Modern data teams have all the right solutions in place to ensure that data is ingested, stored, transformed, and loaded into their data warehouse, but what happens at “the last mile?” In other words, how can data analysts and engineers ensure that transformed, actionable data is actually available to access and use?

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ETL Tools Data Warehouse BI Data Pipeline 52

Monte Carlo

FEBRUARY 15, 2023

Cloud data warehouses solve these problems. Belonging to the category of OLAP (online analytical processing) databases, popular data warehouses like Snowflake, Redshift and Big Query can query one billion rows in less than a minute. What is a data warehouse?

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Data Warehouse Unstructured Data AWS Business Intelligence 52

Grouparoo

AUGUST 24, 2021

What you really want is a unified view of your data using Customer Data Integration so you can take action on it. Customer data integration here might include creating a data warehouse where you can house your accurate and complete dataset. Scalability A data warehouse can scale well with your data.

Data Integration 52

Data Integration Data Consolidation Data Warehouse ETL Tools 52

AltexSoft

JANUARY 26, 2022

For more detailed information on data science team roles, check our video. An analytics engineer is a modern data team member that is responsible for modeling data to provide clean, accurate datasets so that different users within the company can work with them. Here’s the video explaining how data engineers work.

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Engineering Software Engineer Software Engineering Data Warehouse 59

Grouparoo

DECEMBER 7, 2021

Data tokenization techniques allow the storage of critical data in secure locations while data warehouses store a token that points to the secure copy. This enables the application of security controls and protection techniques to a subset of data, transparent to processes accessing the data warehouse.

Data Integration 52

Data Integration Manufacturing ETL Tools Transportation 52

Knowledge Hut

JANUARY 30, 2024

This data can be structured, semi-structured, or entirely unstructured, making it a versatile tool for collecting information from various origins. The extracted data is then duplicated or transferred to a designated destination, often a data warehouse optimized for Online Analytical Processing (OLAP).

ETL Tools 52

ETL Tools Database-centric Data Mining Raw Data 52

Knowledge Hut

MARCH 13, 2024

This is done by specific data analyzing algorithms implemented into the data models to analyze the data efficiently. Maintenance: Bugs are common when dealing with different sizes and types of datasets. Thus, the role demands prior experience in handling large volumes of data. Salary: $105,000 - $125,000 3.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

Knowledge Hut

MARCH 13, 2024

This is done by specific data analyzing algorithms implemented into the data models to analyze the data efficiently. Maintenance: Bugs are common when dealing with different sizes and types of datasets. Thus, the role demands prior experience in handling large volumes of data. Salary: $135,000 - $165,000 2.Big

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

ProjectPro

FEBRUARY 18, 2022

Why is ETL used in Data Science? ETL stands for Extract, Transform, and Load. It entails gathering data from numerous sources, converting it, and then storing it in a new single data warehouse. Supports data migration to a data warehouse from existing systems, etc.

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Project AWS Kafka Healthcare 52

DareData

JULY 5, 2023

The architecture of a data lake project may contain multiple components, including the Data Lake itself, one or multiple Data Warehouses or one or multiple Data Marts. The Data Lake acts as the central repository for aggregating data from diverse sources in its raw format.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Scala 64

Scala Data Lake Machine Learning BI 64

Cloudera

JUNE 16, 2022

Reduce ingest latency and complexity: Multiple point solutions were needed to move data from different data sources to downstream systems. These data products can be web applications, dashboards, alerting systems, or even data science notebooks. . Better yet, it works in any cloud environment.

Kafka 88

Kafka Manufacturing Data Lake SQL 88

Edureka

JULY 16, 2024

Introduction Amazon Redshift, a cloud data warehouse service from Amazon Web Services (AWS), will directly query your structured and semi-structured data with SQL. Amazon Redshift Serverless allows customers to analyze and query data without configuring and managing a data warehouse.

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AWS Data Warehouse Amazon Web Services Business Intelligence 40

Knowledge Hut

NOVEMBER 17, 2023

Here is a step-by-step guide on how to become an Azure Data Engineer: 1. Understanding SQL You must be able to write and optimize SQL queries because you will be dealing with enormous datasets as an Azure Data Engineer. You should be able to create scalable, effective programming that can work with big datasets.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

Edureka

JULY 4, 2024

Overwhelmed with log files and sensor data? It is a cloud-based service by Amazon Web Services (AWS) that simplifies processing large, distributed datasets using popular open-source frameworks, including Apache Hadoop and Spark. Businesses can run these workflows on a recurring basis, which keeps data fresh and analysis-ready.

AWS 52

AWS Amazon Web Services Hadoop Big Data 52

Edureka

JULY 3, 2024

Datasets Datasets in Azure Data Factory define the schema and location of data sources or sinks. They represent the data you want to work with and are used in activities within pipelines. This process ensures that consolidated and consistent data is available for building comprehensive reports and dashboards.

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Pipeline-centric Data Lake Database-centric Data Pipeline 52

dbt Developer Hub

JUNE 12, 2023

Introduction ​ Most data modeling approaches for customer segmentation are based on a wide table with user attributes. This table only stores the current attributes for each user, and is then loaded into the various SaaS platforms via Reverse ETL tools.

Building 59

Building ETL Tools Machine Learning Data Warehouse 59

Edureka

AUGUST 29, 2024

What Does a Data Engineer Do? Data engineers play a paramount role in the organization by transforming raw data into valuable insights. Their roles are expounded below: Acquire Datasets: It is about acquiring datasets that are focused on defined business objectives to drive out relevant insight.

Data Engineering 40

Data Engineering Data Engineer Engineering Generalist 40

ProjectPro

DECEMBER 7, 2021

A pipeline may include filtering, normalizing, and data consolidation to provide desired data. It can also consist of simple or advanced processes like ETL (Extract, Transform and Load) or handle training datasets in machine learning applications. What is a Big Data Pipeline?

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Databand.ai

JULY 5, 2023

Improved Collaboration Among Teams Data engineering teams frequently collaborate with other departments, such as analysts or scientists, who depend on accurate datasets for their tasks. Boosting Operational Efficiency A well-monitored data pipeline can significantly increase an organization’s operational efficiency.

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AltexSoft

AUGUST 29, 2023

The term data lake itself is metaphorical, evoking an image of a large body of water fed by multiple streams, each bringing new data to be stored and analyzed. Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Ascend.io

SEPTEMBER 19, 2023

Transform: Process the data to make it suitable for analysis (this can involve cleaning, aggregating, enriching, and restructuring). Load: Deliver the transformed data into a destination, typically a database or data warehouse.

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ETL Tools ETL Method Data Engineering Data Engineer 52

Meltano

OCTOBER 5, 2022

The responsibilities of a DataOps engineer include: Building and optimizing data pipelines to facilitate the extraction of data from multiple sources and load it into data warehouses. A DataOps engineer must be familiar with extract, load, transform (ELT) and extract, transform, load (ETL) tools.

Engineering 52

Engineering Raw Data Data Pipeline Data Warehouse 52

Edureka

JULY 5, 2024

Tableau Prep has brought in a new perspective where novice IT users and power users who are not backward faithfully can use drag and drop interfaces, visual data preparation workflows, etc., simultaneously making raw data efficient to form insights. Connecting to Data Begin by selecting your dataset.

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Data Preparation Process BI ETL Tools 40

Edureka

JULY 4, 2024

The platform’s massive parallel processing (MPP) architecture empowers you with high-performance querying of even massive datasets. Polyglot Data Processing Synapse speaks your language! This flexibility allows your data team to leverage their existing skills and preferred tools, boosting productivity.

Data Lake 40

Data Lake Pipeline-centric Data Warehouse ETL Tools 40

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.

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Rockset

DECEMBER 9, 2021

Companies that embraced the modern data stack reaped the rewards, namely the ability to make even smarter decisions with even larger datasets. Now more than ten years old, the modern data stack is ripe for innovation. Real-time insights delivered straight to users, i.e. the modern real-time data stack. The problem?

Transportation 52

Transportation BI SQL Database 52

Rockset

JANUARY 28, 2022

Wealth of Data, Little Observability Those metrics included the data generated by existing and new Seesaw users as they interacted with the service. Storing all of that data was not a problem. Seesaw was able to scale up its main database, an Amazon DynamoDB cloud-based service optimized for large datasets.

NoSQL 52

NoSQL PostgreSQL MongoDB ETL Tools 52

Grouparoo

NOVEMBER 16, 2021

The first step in transformation is structural transformations that convert data to a standard format, typically including flattening hierarchical structures and adding missing records. To goal is to create a consistent and coherent dataset compatible with analytical applications and services. featured image via unsplash

Data Mining 52

Data Mining Raw Data ETL Tools Data 52

Knowledge Hut

NOVEMBER 28, 2023

Data Pipelines Data lakes continue to get new names in the same year, and it becomes imperative for data engineers to supplement their skills with data pipelines that help them work comprehensively with real-time streams, daily occurrence raw data, and data warehouse queries.

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

Knowledge Hut

JANUARY 29, 2024

Examples of Data Wrangling Data wrangling can be applied in various scenarios, making it a versatile and valuable process. Here are some common examples: Merging Data Sources : Combining data from multiple sources into one cohesive dataset for analysis, facilitating comprehensive insights.

Raw Data 52

Raw Data Data Mining Data Preparation Structured Data 52

ProjectPro

JANUARY 19, 2022

ETL (extract, transform, and load) techniques move data from databases and other systems into a single hub, such as a data warehouse. Get familiar with popular ETL tools like Xplenty, Stitch, Alooma, etc. Different methods are used to store different types of data.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Storage 52

Edureka

SEPTEMBER 3, 2024

It backs up and restores relational DBMS, NoSQL, data warehouses, and any other data repository types. The actual mapping and transformation work will be performed using the AWS SCT tool, and a small percentage of manual intervention could be required to map the complex schemas. Is AWS DMS an ETL tool?

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