Cloudera

MARCH 2, 2023

Recently, we announced enhanced multi-function analytics support in Cloudera Data Platform (CDP) with Apache Iceberg. It allows multiple data processing engines, such as Flink, NiFi, Spark, Hive, and Impala to access and analyze data in simple, familiar SQL tables. This enables you to maximize utilization of streaming data at scale.

Snapshot 112

Snapshot Data Processing Metadata Management 112

Cloudera

JUNE 7, 2021

At the end of March, we released the first version of Cloudera SQL StreamBuilder as part of CSA 1.3. It enabled users to easily write, run and manage real-time SQL queries on streams from Apache Kafka with an exceptionally smooth user experience. . Flink SQL DDL and Catalog support. Flink SQL DDL Support.

Analytics 100

Analytics Management IT 100

AWS Big Data

SEPTEMBER 13, 2023

In streaming applications, a prevalent approach involves ingesting data through Apache Kafka and processing it with Apache Spark Structured Streaming. For this post, we use the kafka-console-producer.sh Kafka console producer client. For this post, we use the kafka-console-producer.sh

Management 87

Management Data Processing Interactive Metadata 87

Cloudera

AUGUST 10, 2021

Enhanced analytic features that deliver faster SQL queries and ETL with Hive on Tez and Impala, improved Spark with support for Spark 3.1 Apache Ozone enhancements deliver full High Availability providing customers with enterprise-grade object storage and compatibility with Hadoop Compatible File System and S3 API. . x, and 6.3.x,

Metadata 96

Metadata Sales Statistics Management 96

Cloudera

FEBRUARY 22, 2023

Cloudera’s SQL Stream Builder (SSB) is a versatile platform for data analytics using SQL. As apart of Cloudera Streaming Analytics it enables users to easily write, run, and manage real-time SQL queries on streams with a smooth user experience, while it attempts to expose the full power of Apache Flink.

Broadcasting 81

Broadcasting Testing IT Analytics 81

Cloudera

JUNE 28, 2022

We discussed how Cloudera Stream Processing (CSP) with Apache Kafka and Apache Flink could be used to process this data in real time and at scale. The scored transactions are written to the Kafka topic that will feed the real-time analytics process that runs on Apache Flink.

Dashboards 80

Dashboards Machine Learning Statistics KPI 80

Cloudera

JULY 21, 2022

Cloudera has partnered with Rill Data, an expert in metrics at any scale, as Cloudera’s preferred ISV partner to provide technical expertise and support services for Apache Druid customers. We want Cloudera customers that rely on Apache Druid to know that their clusters are secure and supported by the Cloudera partner ecosystem.

Metrics 82

Metrics Slice and Dice Data Warehouse Dashboards 82

AWS Big Data

MARCH 27, 2023

Event streaming is the central nervous system of a data in motion strategy and, in many organizations, Apache Kafka is the tool that powers it. Today, Kafka is well known and widely used for streaming data. However, managing and operating Kafka at scale can still be challenging.

Visualization 72

Visualization Data Lake Interactive Data-driven 72

AWS Big Data

JANUARY 8, 2024

You can also ingest data from third-party platforms such as Apache Spark and Apache Kafka Connect Stream storage – Kinesis Data Streams offer two modes to support the data throughput: On-Demand and Provisioned. All these architecture patterns are integrated with Amazon Kinesis Data Streams.

Analytics 109

Analytics IoT Data-driven Snapshot 109

Cloudera

APRIL 29, 2021

Cloudera released a lot of things around Apache NiFi recently! that provides Apache NiFi on top of Cloudera Data Platform (CDP) 7.1.6. This major release provides the latest and greatest of Apache NiFi as it includes Apache NiFi 1.13.2 We just released Cloudera Flow Management (CFM) 2.1.1 Cloudera also released CDP 7.2.9

Metrics 72

Metrics Data Lake Dashboards Reporting 72

Jen Stirrup

MAY 5, 2020

In this topic, we’re going to focus specifically on Big Data technologies, such as your HDInsight, Apache Spark and Databricks. For example, we can use HBase, which is a no SQL distributed database which offers random access to large amounts of data. We can also use Apache Spark for high-performance complex workloads.

Machine Learning 89

Machine Learning Data Science Data Lake Big Data 89

Cloudera

AUGUST 10, 2021

With Apache Ozone on the Cloudera Data Platform (CDP) , they can implement a scale-out model and build out their next generation storage architecture without sacrificing security, governance and lineage. In this article, we’ll focus on generating and viewing lineage that includes Ozone assets from Apache Atlas. With CDP 7.1.4

Testing 103

Testing Metadata IT Modeling 103

Cloudera

SEPTEMBER 28, 2020

The customer also wanted to utilize the new features in CDP PvC Base like Apache Ranger for dynamic policies, Apache Atlas for lineage, comprehensive Kafka streaming services and Hive 3 features that are not available in legacy CDH versions. ACID transactions, ANSI 2016 SQL SupportMajor Performance improvements.

Testing 130

Testing Metadata Risk Data Science 130

Cloudera

NOVEMBER 10, 2021

This blog post provides CDH users with a quick overview of Ranger as a Sentry replacement for Hadoop SQL policies in CDP. Apache Sentry is a role-based authorization module for specific components in Hadoop. Furthermore, Apache Ranger now supports Public Cloud objects stores like Amazon S3 and Azure Data Lake Store (ADLS).

Data Lake 74

Data Lake Management Metadata Modeling 74

Cloudera

SEPTEMBER 23, 2020

Access audits are mastered centrally in Apache Ranger which provides comprehensive non-repudiable audit log for every access event to every resource with rich access event metadata such as: IP. Actual query run. For access audits the most recent 90-days worth of audits is indexed and can be accessed in the Apache Ranger UI. .

Data Lake 80

Data Lake Metadata IoT Enterprise 80

Cloudera

FEBRUARY 16, 2021

COD is an operational database-as-a-service that brings ease of use and flexibility to Apache HBase. Cloudera Shared Data Experience (SDX) Data Lake helps you configure and manage authorization and access control through the Apache Ranger user interface that ensures consistent policy administration for Apache HBase in COD.

Machine Learning 81

Machine Learning Data Lake Enterprise Data Warehouse 81

Sisense

OCTOBER 15, 2020

However, cloud object storage, like Amazon S3, is a very cost-effective solution (starting at $23 per month per terabyte for hot storage at time of writing) compared to traditional databases and Kafka (which creates three replicas by default on local storage — that’s a lot of data being stored!). billion market by 2025.

Dashboards 98

Dashboards IoT Optimization Internet of Things 98

AWS Big Data

JUNE 28, 2023

With Amazon Redshift Streaming Ingestion, organizations can configure Amazon Redshift to directly ingest high-throughput streaming data from Amazon Managed Streaming for Apache Kafka (Amazon MSK) or Amazon Kinesis Data Streams and make it available for near-real-time analytics in just a few seconds. Challenges with ETL What is ETL?

Analytics 66

Analytics Data Warehouse Data Lake Data-driven 66

Cloudera

OCTOBER 28, 2020

Converting flows to records and filtering using SQL. SQL filtering on Records. Logs are ingested and converted to records, These records are then searched upon using SQL. Additionally, both Ranger and Apache Solr are assumed to be installed, as the two main services chosen to leverage for log ingestion in the NiFi canvas.

Cost-Benefit 63

Cost-Benefit Metadata Consulting Testing 63

AWS Big Data

JULY 27, 2023

Tens of thousands of customers rely on Amazon Redshift to analyze exabytes of data and run complex analytical queries, making it the widely used cloud data warehouse. Launch the Query Editor v2 from the Amazon Redshift console or use your preferred SQL client to connect to your Amazon Redshift cluster for the next steps.

Data Warehouse 80

Data Warehouse Analytics Metadata Dashboards 80

AWS Big Data

MARCH 7, 2023

Data can also be shared among all these repositories without physically moving with features, such as data sharing and federated queries. Data outbound Data is often consumed using structured queries for analytical needs. This section consists of components to query the data, export, exchange, and APIs.

Analytics 68

Analytics Data Warehouse Data Lake Metadata 68

Sisense

MARCH 20, 2019

Data engineers also need to have in-depth database knowledge of SQL and NoSQL since one of the main requirements of the job will be to collect, store, and query information from these databases in real-time. SQL : Learn how to communicate with relational databases through SQL. Python and R.

Big Data 72

Big Data Machine Learning Data Warehouse Unstructured Data 72

AWS Big Data

FEBRUARY 21, 2024

Towards the end of 2022, AWS announced the general availability of real-time streaming ingestion to Amazon Redshift for Amazon Kinesis Data Streams and Amazon Managed Streaming for Apache Kafka (Amazon MSK) , eliminating the need to stage streaming data in Amazon Simple Storage Service (Amazon S3) before ingesting it into Amazon Redshift.

Analytics 103

Analytics Management Data-driven Data Integration 103

AWS Big Data

JUNE 29, 2023

In this post, we discuss why AWS recommends moving from Kinesis Data Analytics for SQL Applications to Amazon Kinesis Data Analytics for Apache Flink to take advantage of Apache Flink’s advanced streaming capabilities. Kinesis Data Analytics for Apache Flink is a fully managed Apache Flink service.

Data Analytics 71

Data Analytics Analytics IoT Data Lake 71

AWS Big Data

JULY 28, 2023

Apache Flink and Apache Spark are both open-source, distributed data processing frameworks used widely for big data processing and analytics. Examples cover code snippets in Python and SQL for both frameworks across three major themes: data preparation, data processing, and data enrichment.

Data Processing 79

Data Processing Big Data Data Quality Technology 79

AWS Big Data

MARCH 11, 2024

Tens of thousands of customers rely on Amazon Redshift to analyze exabytes of data and run complex analytical queries, making it the most widely used cloud data warehouse. As of this writing, MSK records containing compressed data can’t be directly queried in Amazon Redshift. An MSK cluster.

Analytics 65

Analytics Data Warehouse Optimization Metrics 65

AWS Big Data

MARCH 27, 2024

With support for querying cross-account Apache Iceberg tables via Amazon Redshift, you can now build a more comprehensive patient-360 analysis by querying all patient data from one place. The following diagram depicts the high-level solution we build to achieve these outcomes.

Data Analytics 87

Data Analytics Analytics Data Warehouse Data Lake 87

AWS Big Data

JUNE 12, 2023

One notable trend in the streaming solutions market is the widespread use of Apache Kafka for data ingestion and Apache Spark for streaming processing across industries. Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed Apache Kafka service that offers a seamless way to ingest and process streaming data.

Management 75

Management Metadata Testing Internet of Things 75

Cloudera

AUGUST 10, 2022

Cloudera Stream Processing (CSP), powered by Apache Flink and Apache Kafka, provides a complete stream management and stateful processing solution. In CSP, Kafka serves as the storage streaming substrate, and Flink as the core in-stream processing engine that supports SQL and REST interfaces. Apache Kafka and SMM.

Dashboards 85

Dashboards Data-driven Statistics Management 85

AWS Big Data

MARCH 11, 2024

When running Apache Flink applications on Amazon Managed Service for Apache Flink , you have the unique benefit of taking advantage of its serverless nature. With Managed Service for Apache Flink, you can add and remove compute with the click of a button.

Management 93

Management Snapshot Metrics Cost-Benefit 93

AWS Big Data

MARCH 28, 2024

The main challenges were that this system was hard to debug and also queries represented the current state of machines but not the state transitions. This post shows how Krones built a streaming solution to monitor their lines, based on Amazon Kinesis and Amazon Managed Service for Apache Flink. So how to detect a failure?

Management 77

Management Broadcasting IoT Metrics 77

AWS Big Data

NOVEMBER 13, 2023

Amazon Redshift enables you to use SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and machine learning (ML) to deliver the best price-performance at scale. These query patterns and concurrency were unpredictable in nature.

Data Warehouse 88

Data Warehouse Data Lake Analytics Data Science 88

AWS Big Data

JULY 20, 2023

In this post, we describe Orca’s journey building a transactional data lake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics. We explore why Orca chose to build a transactional data lake and examine the key considerations that guided the selection of Apache Iceberg as the preferred table format.

Data Lake 84

Data Lake Analytics Snapshot Optimization 84

AWS Big Data

JUNE 9, 2023

This data is then projected into analytics services such as data warehouses, search systems, stream processors, query editors, notebooks, and machine learning (ML) models through direct access, real-time, and batch workflows. Apache Iceberg 1.2.0, and Delta Lake 2.3.0.

Data Lake 109

Data Lake Metadata Optimization Statistics 109

Ontotext

JANUARY 20, 2022

But although RDF allows you to build knowledge graphs across datasets with federated queries, there is still a large number of databases that do not run on RDF. First, we’ll introduce our tools – GraphQL, Federation and Kafka. We are strong believers in the expressivity and flexibility of SPARQL, the RDF query language.

Enterprise 52

Enterprise Unstructured Data Modeling Visualization 52

Cloudera

NOVEMBER 2, 2020

200,000 queries per day. So they needed a data warehouse that could keep up with the scale of modern big data systems , but provide the semantics and query performance of a traditional relational database. Target per-query SLA of 1 second. Average query concurrency factor of 10. They chose to build their RTDW on Cloudera.

Data Warehouse 90

Data Warehouse Optimization Dashboards Interactive 90

Cloudera

SEPTEMBER 7, 2022

We are now well into 2022 and the megatrends that drove the last decade in data — The Apache Software Foundation as a primary innovation vehicle for big data, the arrival of cloud computing, and the debut of cheap distributed storage — have now converged and offer clear patterns for competitive advantage for vendors and value for customers.

Big Data 107

Big Data Software Enterprise Digital Transformation 107