Data Catalogs Emerge as Strategic Requirement for Data Lakes
If the exhibitors at last week’s Strata + Hadoop World expo are any indication of what’s happening down on the street, data cataloging is evolving from a nice-to-have into a necessity for organizations looking to capitalize on big data.
Hadoop’s so-called “junk drawer” problem has been well-documented. It stems largely from the flexible schema-on-read approach, where data is structured only when it’s finally accessed from the data lake, as opposed to the traditional ETL approach of transforming data when it’s originally loaded into the data warehouse.
In short, getting data into Hadoop is easy, but finding it and getting it back out again can be hard. All sorts of vendors are now looking to address this dilemma, which touches many aspects of big data analytics, including data quality and security. Having a catalog of the data stored in Hadoop seems like a good idea, and there are a number of vendors providing that.
Alex Gorelik, CEO and founder of Waterline Data, which provides data cataloging software for Hadoop and other big data systems, says data professionals are reluctant to open Hadoop to downstream users without a better accounting of the actual data.
“The data lake looks like a flea market,” Gorelik tells Datanami. “It’s all in there somewhere, but how do you find it? It’s a problem for data scientists and data stewards because they can’t give people access until they know what’s in there.”
Gorelik says that while open source tools like Apache Atlas, which is backed by Hortonworks (NASDAQ: HDP), and Cloudera Navigator provide a good technical foundation for addressing data cataloging and master data management (MDM) challenges, they don’t go far enough to solve the problem. Waterline addresses the problem by using “tags” to track the lineage of every piece of data.
With Waterline, Hadoop users can continue ingesting data as they did before, while relying on the software to keep it somewhat organized. Apache Lucene sits under the covers to power searches, while an Amazon-like user interface and “shopping cart” process lets analysts check-out when they’ve found their data.
It’s not a license to be messy with your data, but at least it takes the burden off of users to manually track their data. “People used to have careful directories. But these days, they can’t keep track of their directories,” Gorelik says. “You have millions of files. You should organize them as well as you can. [With Waterline software] it doesn’t matter where the file is, as long as you can find it.”
Collibra is another master data management (MDM) software vendor helping customers keep track of their Hadoop-resident data using the catalog approach. The company, which recently moved its headquarters to New York City, has an eight-year history of providing data governance solution to customers in healthcare, financial services, and other industries.
“What we have is a technology platform that has the capability to keep track of processes around data, the metadata and organization and roles and responsibility for data,” Daniel Sholler, director of product marketing at Collibra, tells Datanami. “We keep track of all the technical connections of all the data because you need to know that stuff. But it turns out that stuff isn’t the interesting stuff.”
Instead, Collibra exposes a set of applications that make it relatively easy for end users to get access to data, if they are authorized to access it. That’s the “interesting” stuff that Sholler was referring to. Data access is one component of a collection of data governance solutions that Collibra is offering, and the scope of that offering will expand in the coming weeks.
Another vendor that’s plying the fruitful waters of data cataloging is Alation. The company originally designed its product to “learn” about data connections by observing how analysts interact. But just providing data cataloging wasn’t enough, the company says. So last week Alation announced that in its version 4.0 update, it will also track queries that run along with the data that’s collected.
Tracking queries and data, says Alation CTO Venky Ganti, will provide critical context that’s required for addressing the needs of data stewards and customers, including answering questions like “Where can I find data to answer my question?” “Can I trust this data?” “What are the data semantics in order to use it?” and “Who can answer my question about this data set.”
“Experts who understand certain datasets often play the stewardship role of ensuring that data consumers can make accurate and effective use of data,” Ganti says in a blog post. “More recently, data governance initiatives have started to assign formal stewardship responsibility.”
Other companies offering data cataloging functionality include Podium Data, which announced a $9.5-million Series A round just prior to the show. Zaloni also unveiled its Bedrock Data Lake Manager (DLM) product, which uses data cataloging to help manage storage more effectively. At Strata, it launched a new version of Mica, its data preparation tool, which introduces a new “shopping cart”-like experience.
That “shopping cart” metaphor was heard often on the Strata expo floor during discussions of data catalogs and big data management. You can expect to see that show up in MDM and data quality tools more often.
Informatica, the big dog of last-gen ETL tools that’s hungering for a piece of the big data pie, also updated its data lake management product, called Data Lake Management, to include more capabilities. Specifically, the product combines data cataloging, stream data capture, Hadoop job management, security, and cloud connectors in a single unified product.
The lack of a centralized data lake management point eats up analysts’ time and hurts productivity, says Amit Walia, executive vice president and chief product officer for Informatica. “Ease of use and a delightful user experience along with robust governance and metadata capabilities are critical for getting business value out of data lakes,” he says in a statement.”
According to Gartner analysts Guido De Simoni and Roxane Edjlali, enterprise metadata management, including data cataloging, has become a “required discipline.” “Failure to recognize this will lead to sustained siloed behavior and loss of business value,” they wrote earlier this year.
While data silos will inevitably be with us for a while, we don’t have to behave as if the data is trapped in a single location. As the Gartner analysts rightly point out, organizations that can get a unified view of their data will find greater business value. It’s becoming clear that data catalogs will be one way of providing that visibility.
September 19, 2017
- TIBCO Connected Intelligence Cloud Equips Companies for Digital Transformation
- Actian Vector in Hadoop Turbocharges Spark Performance
- Kyvos Insights to Showcase Kyvos 4.0 at Strata Data Conference
- Syncsort Announces Trillium Quality for Big Data
- Mesosphere Joins Dell EMC’s Reseller Program
- Machine Learning Makes SAP S/4HANA More Intelligent
- Qumulo Launches New Qumulo File Fabric on AWS
- GigaSpaces Announces Next Generation of In-Memory Computing Platform
- WANdisco Launches Hybrid Data Lake Architecture in Collaboration with AWS
- Teradata Real-Time Insight Benefits Fortune 150 Refining Business
- ExtraHop Unveils Immersive Maps of Digital Enterprise
- DimensionalMechanics Unveils NeoPulse AI Studio on AWS Marketplace
- Bright Computing Announces Support for Ubuntu
September 18, 2017
- Inspur Releases New Generation of Integrated Gene-appliance with Three-fold Performance Increase
- Deutsche Telekom Chooses SAS Customer Intelligence for Marketing Analytics
- Qognify Selected to Globally Secure a Leading Banking Institution
- Seagate and Baidu to Cooperate on Big Data Analysis and Advanced Storage Implementation
- DriveScale Customers Present Use Cases at Strata Data New York 2017
September 14, 2017
Most Read Features
- Taking the Data Scientist Out of Data Science
- Forrester Reshuffles the Deck on BI and Analytics Tools
- Machine Learning: Are You Ready? A 7-Part Checklist
- 9 Must-Have Skills to Land Top Big Data Jobs in 2015
- Which Type of SSD is Best: SATA, SAS, or PCIe?
- Kafka Gets Streaming SQL Engine, KSQL
- Machine Learning, Deep Learning, and AI: What’s the Difference?
- Spark Streaming: What Is It and Who’s Using It?
- The Data Science Behind Dollar Shave Club
- Apple Puts a ‘Neural Engine’ Inside the iPhone
- More Features…
Most Read News In Brief
- How AI Fares in Gartner’s Latest Hype Cycle
- Analytics Spending Up, Trust in Data Down
- Baidu’s AI Algorithm Parses Video
- Anaconda Taps Containers to Simplify Data Science Deployments
- ‘Database Learning’ Aims to Speed Queries
- Microsoft Surges in Gartner Quadrant with Power BI
- RDBMS Remains Popular As Data Sources Grow
- AI Seen Better Suited to IoT Than Big Data
- Databricks, Flush With Cash, Steers Spark at AI
- Tableau Automates K-Means Clustering in V10 Refresh
- More News In Brief…
Most Read This Just In
- UC Irvine Introduces Machine and Deep Learning Programs
- Graph Databases Lie at the Heart of $7 Trillion Self-Driving Car Opportunity
- Arrow Electronics Enables Sensor-to-Cloud-to-Analytics IoT Platform
- GridGain Systems Ranks #187 on Inc. 500 List
- Report: SAS Ranks No. 1 in Advanced, Predictive Analytics Market Share
- MapR Receives $56M Equity Raise from Existing Investors
- Snowflake Introduces Cloud Data Warehouse Built for Financial Services
- Forrester Names TIBCO Leader in Streaming Analytics
- Dataiku Raises $28M Series B to Help Democratize Data Science, Analytics
- Unisys Predictive Freight Solution Wins Global ICMG Award
- More This Just In…
September 17 - September 20San Francisco CA United States
September 20 @ 10:00 am - 11:00 am
September 25 - September 28
September 25 - September 28New York United States
September 26Dallas TX United States
October 31 - November 2Santa Clara CA United States