Smart Data Collective

AUGUST 17, 2022

You can safely use an Apache Kafka cluster for seamless data movement from the on-premise hardware solution to the data lake using various cloud services like Amazon’s S3 and others. It will enable you to quickly transform and load the data results into Amazon S3 data lakes or JDBC data stores.

Apache Kafka 109

Apache Kafka ETL Data Lakes AWS 109

Tableau

JUNE 8, 2021

Domain experts, for example, feel they are still overly reliant on core IT to access the data assets they need to make effective business decisions. In all of these conversations there is a sense of inertia: Data warehouses and data lakes feel cumbersome and data pipelines just aren't agile enough.

Tableau 52

Tableau Data Lakes Data Warehouse SQL 52

phData

APRIL 29, 2024

Oracle – The Oracle connector, a database-type connector, enables real-time data transfer of large volumes of data from on-premises or cloud sources to the destination of choice, such as a cloud data lake or data warehouse.

SQL 52

SQL Azure Data Warehouse Cloud Data 52

The MLOps Blog

MARCH 15, 2023

In this post, you will learn about the 10 best data pipeline tools, their pros, cons, and pricing. A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

The MLOps Blog

JANUARY 23, 2023

These tools may have their own versioning system, which can be difficult to integrate with a broader data version control system. For instance, our data lake could contain a variety of relational and non-relational databases, files in different formats, and data stored using different cloud providers. DVC Git LFS neptune.ai

ML 52

ML ML Data Lakes Machine Learning 52

ODSC - Open Data Science

OCTOBER 15, 2023

Cloudera Cloudera is a cloud-based platform that provides businesses with the tools they need to manage and analyze data. They offer a variety of services, including data warehousing, data lakes, and machine learning. The platform includes several features that make it easy to develop and test data pipelines.

Machine Learning 52

Machine Learning Machine Learning Data Pipeline AI 52

ODSC - Open Data Science

FEBRUARY 14, 2023

It also addresses the strategies and best practices for implementing a data mesh. Applying Engineering Best Practices in Data Lakes Architectures Einat Orr | Ceo and Co-Founder | Treeverse This talk examines why agile methodology, continuous integration, and continuous deployment and production monitoring are essential for data lakes.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Alation

MAY 16, 2023

But with the sheer amount of data continually increasing, how can a business make sense of it? Robust data pipelines. What is a Data Pipeline? A data pipeline is a series of processing steps that move data from its source to its destination. The answer?

Data Pipeline 69

Data Pipeline Data Governance Data Lakes Data Warehouse 69

ODSC - Open Data Science

DECEMBER 19, 2023

Data Engineer Data engineers are responsible for the end-to-end process of collecting, storing, and processing data. They use their knowledge of data warehousing, data lakes, and big data technologies to build and maintain data pipelines.

Data Scientist 96

Data Scientist Machine Learning Machine Learning AI 96

Pickl AI

AUGUST 1, 2023

The primary goal of Data Engineering is to transform raw data into a structured and usable format that can be easily accessed, analyzed, and interpreted by Data Scientists, analysts, and other stakeholders. Future of Data Engineering The Data Engineering market will expand from $18.2

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Ocean Protocol

JANUARY 17, 2024

We wanted to professionalize and operationalize the data pipeline, for use by simulation, the bots, and the analytics app. We wanted to extend simulation, into a flow that supported experiments on realtime data and with the possibility of live trading. It’s centered around a data lake with tiers from raw → refined.

Data Pipeline 52

Data Pipeline AI AI Python 52

ODSC - Open Data Science

JANUARY 18, 2024

This individual is responsible for building and maintaining the infrastructure that stores and processes data; the kinds of data can be diverse, but most commonly it will be structured and unstructured data. They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Smart Data Collective

OCTOBER 17, 2022

Data pipelines automatically fetch information from various disparate sources for further consolidation and transformation into high-performing data storage. There are a number of challenges in data storage , which data pipelines can help address. Choosing the right data pipeline solution.

Data Pipeline 130

Data Pipeline Data Warehouse ETL Exploratory Data Analysis 130

Alation

MAY 16, 2023

But with the sheer amount of data continually increasing, how can a business make sense of it? Robust data pipelines. What is a Data Pipeline? A data pipeline is a series of processing steps that move data from its source to its destination. The answer?

Data Pipeline 40

Data Pipeline Data Governance Data Lakes Data Warehouse 40

The MLOps Blog

MAY 17, 2023

We also discuss different types of ETL pipelines for ML use cases and provide real-world examples of their use to help data engineers choose the right one. What is an ETL data pipeline in ML? Xoriant It is common to use ETL data pipeline and data pipeline interchangeably.

ETL 59

ETL Data Pipeline ML ML 59

IBM Journey to AI blog

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

Data Quality 52

Data Quality Data Lakes Data Warehouse Business Intelligence 52

Alation

MARCH 22, 2022

They created each capability as modules, which can either be used independently or together to build automated data pipelines. The table details are extracted from the IDF pipeline information, which then syncs details like column, table, business, and technical metadata. How the IDF Supports a Smarter Data Pipeline.

DataOps 52

DataOps Data Pipeline Data Engineering Data Engineer 52

Precisely

JANUARY 19, 2023

Powerful data integration capabilities bridge the gap between mainframe systems and cloud platforms, replicating changes on the mainframe to cloud data platforms and on-premise databases in real time. Customers can build new channels, offload processing, and drive business insights and intelligence with data analytics and data lakes.

AWS 52

AWS Cloud Computing Data Pipeline Big Data 52

Smart Data Collective

OCTOBER 27, 2021

Druid is specifically designed to support workflows that require fast ad-hoc analytics, concurrency, and instant data visibility are core necessities. It is easy to integrate with any existing data pipelines, and it can also stream data from the most popular message buses such as Amazon Kinesis and Kafka.

Analytics 113

Analytics Analytics Data Warehouse Business Intelligence 113

Precisely

JANUARY 18, 2024

Customers can build new channels, offload processing, and drive business insights and intelligence with data analytics and data lakes.

AWS 52

AWS Artificial Intelligence Artificial Intelligence Cloud Computing 52

IBM Journey to AI blog

SEPTEMBER 19, 2023

By analyzing datasets, data scientists can better understand their potential use in an algorithm or machine learning model. The data science lifecycle Data science is iterative, meaning data scientists form hypotheses and experiment to see if a desired outcome can be achieved using available data.

Data Science 84

Data Science Analytics Analytics Data Scientist 84

Alation

APRIL 18, 2023

This, in turn, helps them to build new data pipelines, solutions, and products, or clean up the data that’s there. It bears mentioning data profiling has evolved tremendously. Data migration Digital transformation is ongoing. To achieve this, these developers need to build data pipelines that migrate data.

Data Profiling 52

Data Profiling Data Quality Data Governance Data Pipeline 52

IBM Journey to AI blog

OCTOBER 16, 2023

How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools. A data store lets a business connect existing data with new data and discover new insights with real-time analytics and business intelligence.

AI 78

AI AI Data Warehouse ML 78

ODSC - Open Data Science

FEBRUARY 2, 2023

Big data isn’t an abstract concept anymore, as so much data comes from social media, healthcare data, and customer records, so knowing how to parse all of that is needed. This pushes into big data as well, as many companies now have significant amounts of data and large data lakes that need analyzing.

Data Science 98

Data Science Data Scientist Computer Science Computer Science 98

IBM Journey to AI blog

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization.

Data Quality 52

Data Quality Data Governance Data Wrangling Data Scientist 52

ODSC - Open Data Science

JANUARY 30, 2024

Not only does it involve the process of collecting, storing, and processing data so that it can be used for analysis and decision-making, but these professionals are responsible for building and maintaining the infrastructure that makes this possible; and so much more. Think of data engineers as the architects of the data ecosystem.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

AWS Machine Learning Blog

FEBRUARY 13, 2024

Let’s demystify this using the following personas and a real-world analogy: Data and ML engineers (owners and producers) – They lay the groundwork by feeding data into the feature store Data scientists (consumers) – They extract and utilize this data to craft their models Data engineers serve as architects sketching the initial blueprint.

AWS 105

AWS ML ML Machine Learning 105

DagsHub

DECEMBER 11, 2023

It does not support the ‘dvc repro’ command to reproduce its data pipeline. DVC Released in 2017, Data Version Control ( DVC for short) is an open-source tool created by iterative. However, these tools have functional gaps for more advanced data workflows. This can also make the learning process challenging.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Database 98

Alation

JANUARY 26, 2023

That’s why many organizations invest in technology to improve data processes, such as a machine learning data pipeline. However, data needs to be easily accessible, usable, and secure to be useful — yet the opposite is too often the case.

Data Governance 52

Data Governance Data Engineering Data Engineer Data Engineering 52

phData

FEBRUARY 14, 2023

Source data formats can only be Parquer, JSON, or Delimited Text (CSV, TSV, etc.). Streamsets Data Collector StreamSets Data Collector Engine is an easy-to-use data pipeline engine for streaming, CDC, and batch ingestion from any source to any destination.

Data Warehouse 59

Data Warehouse Azure AWS Database 59

Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

ODSC - Open Data Science

FEBRUARY 15, 2024

These systems represent data as knowledge graphs and implement graph traversal algorithms to help find content in massive datasets. These systems are not only useful for a wide range of industries, they are fun for data engineers to work on.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Dataversity

APRIL 1, 2021

In the data-driven world we live in today, the field of analytics has become increasingly important to remain competitive in business. In fact, a study by McKinsey Global Institute shows that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and nine times […].

Analytics 69

Analytics Analytics Data Silos Data Lakes 69

Iguazio

JANUARY 16, 2024

The system’s architecture ensures the data flows through the different systems effectively. First, the data lake is fed from a number of data sources. These include conversational data, ATS Data and more.

ML 52

ML ML DataOps Data Scientist 52

Iguazio

JANUARY 16, 2024

The system’s architecture ensures the data flows through the different systems effectively. First, the data lake is fed from a number of data sources. These include conversational data, ATS data, and more.

ML 52

ML ML DataOps Data Scientist 52

Dataconomy

FEBRUARY 23, 2023

This involves creating data validation rules, monitoring data quality, and implementing processes to correct any errors that are identified. Creating data pipelines and workflows Data engineers create data pipelines and workflows that enable data to be collected, processed, and analyzed efficiently.

Data Engineering 91

Data Engineering Data Engineer Data Engineering Data Engineering 91

The MLOps Blog

JUNE 27, 2023

LakeFS LakeFS is an open-source platform that provides data lake versioning and management capabilities. It sits between the data lake and cloud object storage, allowing you to version and control changes to data lakes at scale. Flyte Flyte is a platform for orchestrating ML pipelines at scale.

Machine Learning 134

Machine Learning Machine Learning ML ML 134