Christophe Blefari

JUNE 16, 2023

Why data consumers do not trust your reporting — It is a good illustration of the data journey manifesto. Stakeholders often notice data issues before the data team does. Data warehouses are mutable, this is one of the many root causes proposed by Lucas. This is metrics drift.

Programming Language 130

Programming Language SQL PostgreSQL Data 130

Data Engineering Podcast

MARCH 15, 2021

Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. RudderStack’s smart customer data pipeline is warehouse-first.

Management 100

Management Data Warehouse PostgreSQL Kafka 100

Rockset

MARCH 26, 2023

Folks have definitely tried, and while Apache Kafka® has become the standard for event-driven architectures, it still struggles to replace your everyday PostgreSQL database instance in the modern application stack. Regardless of what the future holds for databases, we need to solve data silo problems.

Cloud 52

Cloud PostgreSQL Kafka Database 52

Data Engineering Podcast

JANUARY 5, 2020

Debezium is an open source platform for reliable change data capture that you can use to build supplemental systems for everything from maintaining audit trails to real-time updates of your data warehouse. What are some of the other options on the market for handling change data capture? Pulsar, Bookkeeper, Pravega)?

Database 100

Database Kafka PostgreSQL MySQL 100

Data Engineering Podcast

MAY 20, 2018

Presto is a distributed SQL engine that allows you to tie all of your information together without having to first aggregate it all into a data warehouse. Kamil Bajda-Pawlikowski co-founded Starburst Data to provide support and tooling for Presto, as well as contributing advanced features back to the project.

PostgreSQL 100

PostgreSQL Hadoop SQL Kafka 100

Edureka

FEBRUARY 9, 2023

Ingest data into one or more Azure services, including Azure Data Lake, Azure Storage, Azure SQL, and Azure DW, and process the data in Azure Databricks. Develop pipelines in ADF that extract, transform, and load data from sources such as Azure SQL, Blob storage, Azure SQL Data Warehouse, write-back tools, and others.

Data Engineer 52

Data Engineer Data Engineering Engineering Amazon Web Services 52

Data Engineering Podcast

AUGUST 6, 2022

Summary The optimal format for storage and retrieval of data is dependent on how it is going to be used. For analytical systems there are decades of investment in data warehouses and various modeling techniques. For analytical systems there are decades of investment in data warehouses and various modeling techniques.

Machine Learning 100

Machine Learning Database MySQL PostgreSQL 100

Striim

JANUARY 4, 2023

Companies need to analyze large volumes of datasets, leading to an increase in data producers and consumers within their IT infrastructures. These companies collect data from production applications and B2B SaaS tools (e.g., This data makes its way into a data repository, like a data warehouse (e.g.,

PostgreSQL 52

PostgreSQL Data Warehouse Data Data Lake 52

Data Engineering Podcast

OCTOBER 9, 2018

Summary One of the most complex aspects of managing data for analytical workloads is moving it from a transactional database into the data warehouse. What if you didn’t have to do that at all? Links MemSQL NewSQL Microsoft SQL Server St. What if you didn’t have to do that at all?

PostgreSQL 100

PostgreSQL BI Machine Learning Data Warehouse 100

Knowledge Hut

NOVEMBER 28, 2023

Concepts of IaaS, PaaS, and SaaS are the trend, and big companies expect data engineers to have the relevant knowledge. Kafka Kafka is one of the most desired open-source messaging and streaming systems that allows you to publish, distribute, and consume data streams. ETL is central to getting your data where you need it.

Data Engineer 52

Data Engineer Data Engineering Engineering Generalist 52

Data Engineering Podcast

DECEMBER 16, 2019

You work hard to make sure that your data is clean, reliable, and reproducible throughout the ingestion pipeline, but what happens when it gets to the data warehouse? Dataform picks up where your ETL jobs leave off, turning raw data into reliable analytics. How is the metadata itself stored and managed in Marquez?

Metadata 100

Metadata PostgreSQL Datasets Data Warehouse 100

Rockset

MARCH 10, 2022

To deliver real-time analytics, companies need a modern technology infrastructure that includes these three things: A real-time data source such as web clickstreams, IoT events produced by sensors, etc. A platform such as Apache Kafka/Confluent , Spark or Amazon Kinesis for publishing that stream of event data.

Data Analytics 52

Data Analytics Data Warehouse Medical MySQL 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

Silectis

JANUARY 3, 2021

Big Data Processing In order to extract value or insights out of big data, one must first process it using big data processing software or frameworks, such as Hadoop. Big Query Google’s cloud data warehouse. Data Integration Combining data from various, disparate sources into one unified view.

Data Engineer 40

Data Engineer Data Engineering Engineering Hadoop 40

Rockset

NOVEMBER 28, 2022

It works with existing streaming systems like Apache Kafka, Amazon Kinesis, and Azure Events Hubs, making it easier than ever to build a real-time data pipeline. Data warehouses were built for batch jobs, so we shouldn’t be surprised by this. This method has a handful of drawbacks. The upshot?

Data Warehouse 52

Data Warehouse PostgreSQL MongoDB SQL 52

Rockset

JUNE 16, 2020

Above all, applications need fast queries on live data to personalize user experiences, provide real-time customer 360s, or detect anomalous situations, as the case may be. Real-Time Architecture Today One of two options is typically used to support these real-time data-driven applications today.

MongoDB 52

MongoDB Data Lake PostgreSQL Kafka 52

Striim

DECEMBER 8, 2023

CDC is a technique designed to efficiently capture and track changes made in a source database, thereby enabling real-time data synchronization and streamlining the process of updating data warehouses, data lakes, or other systems. See enabling Kafka Streams in Striim Platform (self-hosted).

Kafka 64

Kafka Database Data Warehouse Data 64

Zalando Engineering

SEPTEMBER 20, 2015

Named after the Javanese word for “queue,” Saiki is built mostly in Python and includes components that provide a scalable Change Data Capture infrastructure, consume PostgreSQL replication logs, and perform other relevant tasks. presumed the prior integration of data distributed over a significant number of sources.

Data Integration 40

Data Integration PostgreSQL Amazon Web Services Kafka 40

AltexSoft

NOVEMBER 7, 2022

However, the platform is compatible with solutions supporting near real-time and real-time analytics — such as Apache Kafka or Apache Spark. For production purposes, choose from PostgreSQL 10+, MySQL 8+, and MsSQL. The Good and the Bad of Power BI Data Visualization. The Good and the Bad of Hadoop Big Data Framework.

PostgreSQL 52

PostgreSQL Metadata Python MySQL 52

Knowledge Hut

SEPTEMBER 6, 2023

Data Analysis : Strong data analysis skills will help you define ways and strategies to transform data and extract useful insights from the data set. Big Data Frameworks : Familiarity with popular Big Data frameworks such as Hadoop, Apache Spark, Apache Flink, or Kafka are the tools used for data processing.

Big Data 52

Big Data Certification Hadoop Kafka 52

Cloudera

APRIL 15, 2021

Added support for standalone NiFi/Kafka clusters. Hive Warehouse Connector (HWC) makes data engineering simpler and faster. Better Hive-Spark interaction with HWC which makes data engineering applications simpler and more efficient to create. Data Warehouse. We’ve added OS support for RHEL / CentOS 7.9

Cloud 70

Cloud MySQL PostgreSQL SQL 70

Rockset

JULY 25, 2019

encompasses your data pipeline that sources data from various sources deposits it into your data lake or data warehouse runs various transformations to extract insights, and then. In this respect, it is very similar to transactional databases like Oracle, PostgreSQL, etc. titled Concurrency Control ).

Software Engineering 40

Software Engineering Software Engineer Engineering PostgreSQL 40

Rockset

JANUARY 28, 2022

“We had a very disorganized data infrastructure that, as we’ve grown, was getting in the way of helping our sales and marketing and support and customer success teams really service our customers in the way that we wanted to.” Results, even for complex queries, would be returned in milliseconds.

NoSQL 52

NoSQL PostgreSQL MongoDB ETL Tools 52

Rockset

OCTOBER 20, 2021

Until now, the majority of the world’s data transformations have been performed on top of data warehouses, query engines, and other databases which are optimized for storing lots of data and querying them for analytics occasionally. For instance, let’s say you have streaming data coming in from Kafka or Kinesis.

SQL 52

SQL PostgreSQL MongoDB NoSQL 52

Databand.ai

DECEMBER 13, 2022

We’d be remiss not to share that Joseph was a recent guest on Databand’s MAD Data Podcast , where he discussed ways to keep data systems from becoming unwieldy and shared tips for data teams to manage their data warehouses and keep data pipelines running reliably. You can also watch the video recording.

Data Engineer 52

Data Engineer Data Engineering Engineering AWS 52

ProjectPro

JULY 27, 2021

Non-relational databases are ideal if you need flexibility for storing the data since you cannot create documents without having a fixed schema. E.g. PostgreSQL, MySQL, Oracle, Microsoft SQL Server. E.g. Redis, MongoDB, Cassandra, HBase , Neo4j, CouchDB What is data modeling? Data is regularly updated.

Data Engineer 40

Data Engineer Data Engineering Engineering Hadoop 40

Rockset

SEPTEMBER 13, 2022

Rather than individual, transactional updates from your application clients, Rockset is designed for continuous, streaming ingestion from your primary data store. It has direct connectors for a number of primary data stores, including DynamoDB, MongoDB, Kafka, and many relational databases.

SQL 52

SQL Database Relational Database NoSQL 52

ProjectPro

AUGUST 21, 2015

Unorganized and raw data that cannot be categorized as semi-structured or structured data is referred to as unstructured data. are all examples of unstructured data. When compaction takes place, the old data will take the new block size so that the existing data is read correctly.

Hadoop 40

Hadoop Big Data Metadata Java 40

ProjectPro

APRIL 26, 2016

Hcatalog can be used to share data structures with external systems. Hcatalog provides access to hive metastore to users of other tools on Hadoop so that they can read and write data to hive’s data warehouse. HBase is a NoSQL database whereas Hive is a data warehouse framework to process Hadoop jobs.

Hadoop 40

Hadoop Metadata SQL Database 40

Striim

FEBRUARY 14, 2023

If you’d like to get an overview from a data streaming expert first, request a demo here. If you’d like to join our first cohort of Striim Developers, you can sign up here.

PostgreSQL 52

PostgreSQL MongoDB MySQL Kafka 52